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Abstract: Cooling tower emissions are harmful for several reasons such as air polluting, wetting, icing and solid particle deposition, but mainly due to human health hazards (i.e. Legionella). There are several methods for measuring drift drops. This paper is focussed on the sensitive paper technique, which is suitable in low drift scenarios and real conditions. The lack of an automatic classification method motivated the development of a digital image process algorithm for the Sensitive Paper method. This paper presents a detailed description of this method, in which, drop-like elements are identified by means of the Canny edge detector combined with some morphological operations. Afterwards, the application of a J48 decision tree is proposed as one of the most relevant contributions. This classification method allows us to discern between stains whose origin is a drop and stains whose origin is not a drop. The method is applied to a real case and results are presented in terms of drift and PM10 emissions. This involves the calculation of the main features of the droplet distribution at the cooling tower exit surface in terms of drop size distribution data, cumulative mass distribution curve and characteristic drop diameters. The Log-normal and the Rosin–Rammler distribution functions have been fitted to the experimental data collected in the tests and it can been concluded that the first one is the most suitable for experimental data among the functions tested (whereas the second one is less suitable). Realistic PM10 calculations include the measurement of drift emissions and Total Dissolved Solids as well as the size and number of drops. Results are compared to the method proposed by the U.S. Environmental Protection Agency assessing its overestimation. Drift emissions have found to be 0.0517% of the recirculating water, which is over the Spanish standards limit (0.05%). [Copyright &y& Elsevier]

Photochemical air quality models (AQMs) are a vital tool for atmospheric pollution research and have been widely used in various applications, such as air quality prediction and evaluation of pollution control strategies. Before using these models for further studies, it is essential to thoroughly evaluate their reliability and accuracy. While previous guidelines and benchmarks have primarily focused on fine particulate matter (PM 2.5) and ozone (O 3), there is still a lack of benchmarks for evaluating the model performance on primary criteria pollutants such as sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM 10). The use of air quality models in China has increased significantly in the past decades. However, there is still a lack of standardized benchmarks for the performance evaluation of these models. Building upon our previous work on PM 2.5 and its chemical species, we propose a set of benchmarks for evaluating the performance of the aforementioned four air pollutants. Initially, we identified a total of 475 papers published during 2007–2019 that utilized at least one of the five commonly used AQMs in China. From these papers, we selected 164 articles that provided model performance evaluation (MPE) results of the four primary air pollutants. The three most frequently used Model Performance Evaluation (MPE) metrics were selected to analyse the impact of different model configurations on the reported statistics, including modelling region, season, and emission inventory. Lastly, three commonly used statistical indicators, including normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R), were proposed for the validation of simulated NO 2 , SO 2 , CO, and PM 10. Two sets of benchmarks are given, including the "goal" and "criteria". The "goal" represents the best range of performance that a model can be expected to achieve, and the "criteria" represents performance that the majority of studies have achieved. We recommend R values above 0.50, 0.35, 0.45, and 0.40 for NO 2 , SO 2 , PM 10 , and CO, respectively, in order to meet the "criteria" benchmark. If the "goal" benchmark is to be achieved, the corresponding R values are 0.60, 0.55, 0.60, and 0.60. The "goal" benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are within ±20%, ±25%, ±20%, and ±25%, respectively; while the "goal" benchmarks of NME for the four pollutants are less than 40%, 45%, 45%, and 60%, respectively. These benchmarks supplement our previous benchmarks for PM 2.5 and its components and provide a more comprehensive guideline for the air quality modelling community in China. • A set of benchmarks for evaluation of the NO 2 , SO 2 , CO and PM 10 simulation over China are proposed. • Three commonly used statistical indicators (NMB, NME, and R) were proposed for the validation of the four pollutants. • The goal benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are <20%, <25%, <20% and <25%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we applied the Dispersion Normalised Positive Matrix Factorisation (DN-PMF) approach recently proposed in the literature to provide a more realistic picture of the relative importance of emission strength vs. atmospheric dispersion conditions. The disentanglement of such effects is of great concern in pollution hot spots like the Po Valley (Italy), where particulate matter limit values are exceeded despite the existing abatement measures. To explore the potentiality of the DN-PMF approach – still scarcely applied in the literature – a well-chemically characterised PM 1 (atmospheric particles with aerodynamic diameter <1 μm) dataset comprising samples collected at different time resolutions at an urban background site (Bologna) in the southern Po Valley was used. Indeed, it is well known that shallow mixing layers promote pollutant accumulation but this observation is not enough to exclude an enhancement of emission strength which could be tackled by appropriate abatement strategies. The source apportionment of sub-micron sized aerosols having a quite long atmospheric residence time in a complex environment like the Po Valley - which is also strongly impacted by secondary aerosol formation on a basin-scale - is generally quite challenging when using receptor models. Due to the availability of a huge dataset with variables having multiple time resolutions, in this work the DN-PMF was implemented in a multi-time resolution approach (MT) to achieve a better source identification and to gain knowledge about the relative importance of atmospheric dilution vs. emissions. A comparison between results obtained by the application of the regular multi time resolution (REG-MT) vs. the DN-MT approach is presented here for the five factors identified (nitrate-dominated, sulphate-dominated, biomass burning, mineral dust, and urban aerosol). The first interesting outcome is that REG-MT and DN-MT results do not point at significant differences in temporal patterns for aerosol components and sources impacting at the basin-scale (i.e. sulphate- and nitrate-dominated aerosol, biomass burning) thus suggesting that the diel modulation of these PM 1 emissions is somehow masked by the stronger variability of the mixing layer. Conversely, contributions from local sources with more pronounced diel variation like traffic are quite well reproduced by DN-MT and the ambient concentrations are enhanced compared to REG-MT. This is an important piece of information highlighting that PM 1 concentrations from local sources have been likely underestimated by REG-MT assessments. To our knowledge, this is one of the very few applications of DN-MT and the first one at a European site where the huge effort made to implement air pollution containment measures is still not very much effective in reducing PM levels; moreover, in this paper a detailed discussion about the possible interpretation of the output of DN-MT in terms of temporal patterns is reported. [Display omitted] • High PM levels are due to atmospheric stability and emission strength; DN-PMF points out the source emission role. • Secondary aerosol-dominated factors showed a regional nature. • Urban aerosol factor was highly enhanced by dispersion-normalisation. [ABSTRACT FROM AUTHOR]

Accurate prediction of air pollutant concentrations can be effective in controlling and preventing air pollution, which is crucial for both the government's policy response and the public's reasonable ability to avoid air pollution. In this study, we propose a model based on the principle of big data correlation and deep learning techniques for predicting the concentration of air pollutant concentrations. The proposed model, named STARes-SaLSTM, consists of a residual neural network with spatiotemporal attention (STAResNet) and a temporal self-attention based on long short-term memory network (SaLSTM). The spatiotemporal attention module in STAResNet enhances the original input information by weighting it in both spatial and temporal dimensions, meanwhile the features of the spatial distribution of pollutant and meteorological information across many cities are deeply extracted using ResNet. A novel self-attention method known as temporal self-attention is developed to extract the temporal dependency of air pollutants. LSTM is used as a sequence encoder in this temporal attention method to calculate queries, keys, and values to acquire a more comprehensive temporal dependence than is possible with normal self-attention. The STARes-SaLSTM model successfully predicts air pollution concentrations in the target city for the future by extracting spatiotemporal correlation of feature sequence. In comparison to previous neural network models and conventional methods, the model increases the accuracy of pollutant concentration prediction. The suggested prediction model works well for the one to 3 h prediction job, with a root mean square error (RMSE) ranging from 6.716 to 11.648. Furthermore, even for the 1–24 h prediction assignment, we executed multi-scale prediction in the target city and obtained a reasonable performance, with an average RMSE value of 20.576. • This paper solves the problem of difficult extraction of spatiotemporal features. • ResNet allows for the extraction of spatially relevant features; • The spatiotemporal attention mechanism is used to spatiotemporal extract features; • The temporal self-attention mechanism obtain temporal dependence of data. [ABSTRACT FROM AUTHOR]

Air pollution represents a continuous hazard to human health. Administration, companies and population need efficient indicators of the possible effects given by a change in decision, strategy or habit. The monetary quantification of health effects of air pollution through the definition of external costs is increasingly recognized as a useful indicator to support decision and information at all levels. The development of modelling tools for the calculation of external costs can provide support to analysts in the development of consistent and comparable assessments. In this paper, the DIATI Dispersion and Externalities Model (DIDEM) is presented. The DIDEM model calculates the delta-external costs of air pollution comparing two alternative emission scenarios. This tool integrates CALPUFF's advanced dispersion modelling with the latest WHO recommendations on concentration-response functions. The model is based on the impact pathway method. It was designed to work with a fine spatial resolution and a local or national geographic scope. The modular structure allows users to input their own data sets. The DIDEM model was tested on a real case study, represented by a comparative analysis of the district heating system in Turin, Italy. Additional advantages and drawbacks of the tool are discussed in the paper. A comparison with other existing models worldwide is reported. [ABSTRACT FROM AUTHOR]

Estimates of light extinction and visibility are routinely performed by the U.S. Interagency Monitoring of Protected Visual Environments (IMPROVE) network using a simple algorithm which assesses light extinction coefficient (b ext ) at remote and rural sites from concentrations of major particulate matter (PM) species, NO 2 , and Rayleigh scattering from clear-air gaseous components. Following the same approach, in this paper an equation with tailored (i.e. site-specific) coefficients was implemented with the aim of reducing uncertainties and assumptions of the IMPROVE algorithm for applications at polluted urban sites. Major differences compared to IMPROVE algorithm are: 1) dry mass extinction efficiencies calculated using a discrete dipole approximation code with aerosol size distributions measured at our monitoring site as input data; 2) site-specific water growth functions computed separately for ammonium sulfate, ammonium nitrate, and organic matter; 3) fine soil evaluated using an equation previously adopted at our urban site; 4) aerosol absorption component assessed directly through filter-based optical measurements. Details about the calculations performed are reported in the paper and the comparison with the IMPROVE revised algorithm is discussed. The tailored approach here proposed to estimate reconstructed light extinction was applied to PM 2.5 test samples collected on purpose in Milan (Italy), where heavy pollution episodes occur during winter periods. In addition, visual range was calculated applying the Koschmieder equation and compared to visibility measured at the nearby Milano-Linate airport obtaining a fairly good correlation. [ABSTRACT FROM AUTHOR]

Urban greenery is identified as a potential tool in air pollution mitigation. However, the impact is still debated. This paper introduces the innovative VIDA (Vegetation Impact Dynamic Assessment) model, specifically designed to quantify air pollution removal through deposition on vegetation. The VIDA model offers an advanced representation of vegetation that could be integrated into urban air quality dispersion models in the future. Furthermore, the model serves as a valuable tool for exploring the intricate interactions among deposition, resuspension, and wash-off processes, as well as understanding how meteorological conditions and various leaf traits influence these processes. The current version of the model focuses on particulate matter (PM) and encompasses a range of processes, including deposition on vegetation surfaces, encapsulation within the waxy cuticle, wind-driven resuspension, and wash-off. Additionally, the model takes into consideration dynamic changes in PM concentrations on the leaf surface over time, incorporating factors such as PM size fractions, meteorological conditions, and leaf characteristics. This comprehensive approach allows for the evaluation of various species or species groups based on their distinct traits. The VIDA model effectively reproduces measured data, yet continued evaluation remains crucial as new data emerges. Notably, challenges were encountered due to data scarcity and the absence of standardized methods for characterizing vegetation traits. Addressing these challenges and refining the representation of wash-off process will enhance the VIDA model's utility in predicting the dynamic relationship between vegetation and air quality. The introduction of VIDA provides a significant advancement in modelling air pollution removal by deposition to vegetation at a relevant local scale and enables inclusion of urban greenery as tool in urban planning for air pollution mitigation. • The VIDA model quantifies air pollution removal through deposition on vegetation. • VIDA models particle deposition, cuticle encapsulation, resuspension, and wash-off. • VIDA enables inclusion of urban greenery in air pollution mitigation. [ABSTRACT FROM AUTHOR]

While concentrations of most air pollutants have been decreasing in Europe over the last 20 years, O 3 is showing variable trends, with increasing average and decreasing peak concentrations. The complexity of O 3 chemistry adds to the difficulty of understanding both the trends observed and how concentrations can be mitigated. This paper tries to answer the following questions: which emission sectors should be targeted and what levels of reduction could be achieved? To address these reflections, an Atlas of O 3 chemical regimes has been constructed. For this Atlas, 22 European cities were selected and the surrogate model Air Control Toolbox (ACT) was used to evaluate the simulated changes in several ozone metrics as a result of reductions in road transport and industrial emissions. O 3 chemical regimes have been classified and put in perspective with meteorological and emission data at each city location and around. The O 3 sensitivity to road transport and industrial emissions differ from one city to another, but also for the same city when considering different ozone metrics and seasons (e.g., annual means versus SOMO35 or summer peaks). Counterproductive impacts yielding O 3 increase when emissions are reduced are mainly encountered in regions or periods where O 3 concentration are relatively low. In terms of meteorological factors, O 3 chemical regimes are mostly impacted by the amount of solar radiation received but wind speed also has a considerable impact. Most cases show a higher sensitivity to emission reductions from road transport or equal sensitivity to emission reductions from road transport and industry. Very few cases are more sensitive to emissions from the industrial sector. However, the response of annual or seasonal average O 3 metrics to industrial and road transport emissions can be considered relatively low with a maximum reduction of 33% for a 100% reduction of both industrial and road transport emissions. This is because anthropogenic emission can only mitigate ozone above a substantial natural tropospheric background. It is precisely this incremental anthropogenic ozone, which should be targeted by efficient policies as we also demonstrate that none of the cities studied would exceed the European target value with a 100% reduction in emissions. • An atlas of ozone chemical regimes has been constructed for 22 European cities. • O 3 sensitivity to road transport and industrial emissions differ when considering different ozone metrics and different periods of the year. • Counterproductive effects of precursors emission reductions on ozone are a concern where and when ozone concentrations are low. • On the contrary, significant reductions of precursor emissions are found to reduce the number of exceedances of the EU target limit value. • In most cities, mitigation of road transport yields larger benefit to ozone air pollution than mitigation of the industrial sector. [ABSTRACT FROM AUTHOR]

Simplified analytical solutions of fate and transport models are often used to carry out risk assessment on contaminated sites, to evaluate the long-term air quality in relation to volatile organic compounds in either soil or groundwater. Among the different assumptions employed to develop these solutions, in this work we focus on those used in the ASTM-RBCA “box model” for the evaluation of contaminant dispersion in the atmosphere. In this simple model, it is assumed that the contaminant volatilized from the subsurface is dispersed in the atmosphere within a mixing height equal to two meters, i.e. the height of the breathing zone. In certain cases, this simplification could lead to an overestimation of the outdoor air concentration at the point of exposure. In this paper we first discuss the maximum source lengths (in the wind direction) for which the application of the “box model” can be considered acceptable. Specifically, by comparing the results of “box model” with the SCREEN3 model of U.S.EPA we found that under very stable atmospheric conditions (class F) the ASTM-RBCA approach provides acceptable results for source lengths up to 200 m while for very unstable atmospheric conditions (class A and B) the overestimation of the concentrations at the point of the exposure can be already observed for source lengths of only 10 m. In the latter case, the overestimation of the “box model” can be of more than one order of magnitude for source lengths above 500 m. To overcome this limitation, in this paper we introduce a simple analytical solution that can be used for the calculation of the concentration at the point of exposure for large contaminated sites. The method consists in the introduction of an equivalent mixing zone height that allows to account for the dispersion of the contaminants along the source length while keeping the simplistic “box model” approach that is implemented in most of risk assessment tools that are based on the ASTM-RBCA standard (e.g. RBCA toolkit). Based on our testing, we found that the developed model replicates very well the results of the more sophisticated dispersion SCREEN3 model with deviations always below 10%. The key advantage of this approach is that it can be very easily incorporated in the current risk assessment screening tools that are based on the ASTM standards while ensuring a more accurate evaluation of the concentration at the point of exposure. [ABSTRACT FROM AUTHOR]

Flare emissions, especially start-up flare emissions, during chemical plant operations generate large amounts of ozone precursors that may cause highly localized and transient ground-level ozone increment. Such an adverse ozone impact could be aggravated by the synergies of multiple plant start-ups in an industrial zone. In this paper, a systematic study on ozone increment superposition due to chemical plant start-up emissions has been performed. It employs dynamic flaring profiles of two olefin plants’ start-ups to investigate the superposition of the regional 1-hr ozone increment. It also summaries the superposition trend by manipulating the starting time (00:00–10:00) of plant start-up operations and the plant distance (4–32 km). The study indicates that the ozone increment induced by simultaneous start-up emissions from multiple chemical plants generally does not follow the linear superposition of the ozone increment induced by individual plant start-ups. Meanwhile, the trend of such nonlinear superposition related to the temporal (starting time and operating hours of plant start-ups) and spatial (plant distance) factors is also disclosed. This paper couples dynamic simulations of chemical plant start-up operations with air-quality modeling and statistical methods to examine the regional ozone impact. It could be helpful for technical decision support for cost-effective air-quality and industrial flare emission controls. [ABSTRACT FROM AUTHOR]

From November 2012 to July 2013, a sampling campaign was completed for comprehensive characterization of PM 2.5 over four key emission regions in China: Beijing-Tianjin-Hebei (BTH), Yangzi River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SB). A multi-method approach, adopting different analytical and receptor modeling methods, was employed to determine the relative abundances of region-specific air pollution constituents and contributions of emission sources. This paper is focused on organic molecular marker based source apportionment using chemical mass balance (CMB) receptor modeling. Analyses of the organic molecular markers revealed that vehicle emission, coal combustion, biomass burning, meat cooking and natural gas combustion were the major contributors to organic carbon (OC) in PM 2.5 . The vehicle emission dominated the sources contributing to OC in spring at four sampling sites. During wintertime, the coal combustion had highest contribution to OC at BTH site, while the major source contributing to OC at YRD and PRD sites was vehicle emission. In addition, the relative contributions of different emission sources to PM 2.5 mass at a specific location site and in a specific season revealed seasonal and spatial variations across all four sampling locations. The largest contributor to PM 2.5 mass was secondary sulfate (14–17%) in winter at the four sites. The vehicle emission was found to be the major source (14–21%) for PM 2.5 mass at PRD site. The secondary ammonium has minor variation (4–5%) across the sites, confirming the influences of regional emission sources on these sites. The distinct patterns of seasonal and spatial variations of source apportionment observed in this study were consistent with the findings in our previous paper based upon water-soluble ions and carbonaceous fractions. This makes it essential for the local government to make season- and region-specific mitigation strategies for abating PM 2.5 pollution in China. [ABSTRACT FROM AUTHOR]

The impact of airport operations on air quality is a key public health concern for the population surrounding an airport. Air pollution regulations require the assessment of this impact using dispersion models. Modeling dispersion of aircraft-related sources poses challenges because of the large number and variety of airport sources, which include aircraft, ground operation vehicles, and traffic in and out of the airport, most of which are mobile. Emissions from aircraft sources are transient, buoyant, and occur at different heights from the ground. Quantifying these emissions as well as modeling the governing processes is challenging. An added complexity occurs when the airport is situated near a shoreline where meteorological conditions are far from being spatially uniform. These features that characterize the dispersion of airport emissions are being incorporated into the AERMOD model in this paper. This paper examines the impact of shoreline meteorology and urban effects on dispersion by comparing model estimates of SO 2 with corresponding measurements made during a field study conducted at the Los Angeles International Airport (LAX) during winter and summer of 2012 at all the four core sites (AQ, CN, CE, and CS) as a part of the LAX Air Quality Source Apportionment Study (AQSAS). We modified outputs from AERMOD's meteorological preprocessor AERMET to account for 1) the formation of the internal boundary layer that is formed when stable air from the ocean flows onto the warmer land surface of the airport, and 2) urban roughness effects on winds flowing from Los Angeles, east of the airport. Simulations with unmodified AERMET yielded concentrations that were substantially higher than the concentrations at AQ and CS and much lower than those at CN and CE. Model performance improved when AERMOD used the modified meteorology. The fraction of model estimates within a factor of two of the observations improved from 34 to 84% at the CS site and CE site, by up to 79% in winter season whereas in summer, FAC2 values are almost comparable at all the sites. The ratio of robust highest modeled values to measured values improved from 7.72 to 2.53 and 4.92 to 1.94 in winter and summer seasons respectively. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the calculation of personal air pollution exposure during trips made by active transportation using activity patterns without personal monitors. We calculate exposure as the inhaled dose of particulate matter 2.5 μg or smaller. Two modes of active transportation are compared, and they include cycling and walking. Ambient conditions are calculated by combining mobile and stationary monitoring data in an artificial neural network space-time model. The model uses a land use regression framework and has a prediction accuracy of R 2 = 0.78. Exposure is calculated at 10 m or shorter intervals during the trips using inhalation rates associated with both modes. The trips are children’s routes between home and school. The average dose during morning cycling trips was 2.17 μg, during morning walking trips was 3.19 μg, during afternoon cycling trips was 2.19 μg and during afternoon walking trips was 3.23 μg. The cycling trip dose was significantly lower than the walking trip dose. The air pollution exposure during walking or cycling trips could not be strongly predicted by either the school or household ambient conditions, either individually or in combination. Multiple linear regression models regressing both the household and school ambient conditions against the dose were only able to account for, at most, six percent of the variance in the exposure. This paper demonstrates that incorporating activity patterns when calculating exposure can improve the estimate of exposure compared to its calculation from ambient conditions. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to air quality modeling for megacities, focusing on the Paris region for the year 2009. The simulation is conducted by coupling the transport, energy demand, and emission model OLYMPUS with the atmospheric chemical transport model CHIMERE. OLYMPUS's emission calculations are based on the representation of an urban configuration, the simulation of a synthetic population, and the calculation of transport demand and energy consumption in buildings using an activity-based statistical approach. Emissions derived from this simulation, along with biogenic and additional industrial emissions, were used as input data in the CHIMERE model to predict pollutant concentrations. CHIMERE outputs were compared with observations from the local AIRPARIF air quality monitoring network, and with a reference simulation conducted using a benchmark bottom-up emission inventory. The results indicate that the platform provides a comprehensive representation of emissions and the resulting air quality. They also highlight the excellent representation of the spatial and temporal distribution of urban pollutant concentrations in comparison with both model and observational data. OLYMPUS tends to emit more primary pollutants than the reference emission inventory in the dense urban center. However, this often improves model scores for NO 2 , and these deviations remain within the uncertainty margins set for emission inventories. The approach to emission production, rooted in the connection between urban configuration and individual behaviors, allows for the exploration of innovative air quality scenarios centered on energy consumption practices. The validation of our results furnishes the OLYMPUS-CHIMERE coupling with a dependable framework for addressing numerous research inquiries related to the impact of urban policies on air quality. [Display omitted] • Description of an air quality modeling chain for decision-making. • Integration of socio-demographics parameters in the air pollutant emissions and concentrations modeling. • Validation of the modeling chain on Paris megacity. [ABSTRACT FROM AUTHOR]

It is important to be able to predict high concentrations of tropospheric ozone and to inform the population about any violations of air-quality standards, as defined by international regulations. Although first-principle models that cover large geographical regions and different atmospheric layers are improving constantly, they typically still only cover geographical regions with a relatively low resolution. Such model predictions can be problematic for the micro-locations of a complex terrain, i.e., a terrain with a large geographical diversity or urban terrain. For such micro-locations, statistical models can be utilised. This paper presents a modelling and prediction algorithm that can be used in, or in accordance with, a mobile air-quality measurement station. Such a mobile station would enable the set-up of a statistical model and a relatively rapid access to the model's predictions for a specific geographical micro-location without a large quantity of historical of measurements. Uncertainty information about the model's predictions is also usually required. In addition, such a model can adapt to long-term changes, such as climate changes. In the paper we propose Gaussian-process models for the described modelling and prediction. In particular, we selected evolving Gaussian-process models that update on-line with the incoming measurement data. The proposed algorithm for the mobile air-quality measurement and the forecasting station is evaluated on measurements from five locations in Slovenia with different topographical and geographical properties. The obtained evaluation results confirm the feasibility of the concept. [ABSTRACT FROM AUTHOR]

Open burning is a widely practiced method of solid waste disposal in many regions of the world and represents a significant source of air pollution. Black carbon (BC) is a particularly serious air pollutant emitted from the uncontrolled burning of waste in open fires because it has a global warming potential (GWP) up to 5000 times greater than carbon dioxide (CO 2) and is also linked to detrimental health impacts. However, few quantitative measurements of BC from open burning have been completed to establish the extent and impacts of this emission source on the environment. Emission factors (EFs) for BC from burning mixed solid waste samples were measured in the laboratory based on waste compositions in a representative developing country (Mexico). Black carbon EFs were also derived for individual waste types, including: green waste, different types of plastics, textiles and paper and cardboard. Individual waste BC EFs were combined using waste composition data from different areas of the world to estimate regional and global BC emissions from this source. The results demonstrated that BC emissions from open burning of waste have a significant climate impact, equivalent to 2–10% of global CO 2 Eq emissions. Global BC CO 2 Eq emissions from burning waste are 2–8 times larger compared to methane (CH 4) CO 2 Eq emissions arising from the decomposition of equivalent amounts of combustible biodegradable waste disposed at dumpsites. Action to reduce open burning of waste would have a significant and immediate benefit to improving air quality and reducing the potential impact on climate change. • BC EFs were measured for single and mixed waste streams. • BC EFs were in the range of 0.02–53.1 g kg−1, depending strongly on waste type. • BC emissions from open burning of waste were calculated for different regions. • BC emissions from burning waste contributes over 2–10% of global CO 2 Eq emissions. • BC CO 2 Eq from burning is 4 times larger compared to CH 4 from waste decomposition. [ABSTRACT FROM AUTHOR]

Ground-level ozone ( O 3 ) is a major air pollutant which is strongly links to weather conditions. To our knowledge, there are few analysis of O 3 behavior in Caribbean region. The aim of this paper is to study the correlation between O 3 and meteorological parameters (i.e., solar radiation, temperature, relative humidity and wind speed) in a multi-scale way. Firstly, we estimate their Fourier power spectra and consider their scaling properties in the physical space. The power spectra computed follows a power law behavior for all datasets. Thereafter, the multiple scale dynamics and the correlations between simultaneous measurements of O 3 and meteorological parameters was analyzed using the Empirical Mode Decomposition (EMD) based methods. After EMD decomposition of each time series, we investigate their local correlations using a new cross-correlation technique termed Time Dependent Intrinsic Correlation method (TDIC). The time evolution and the scale dependence of their correlation are determined at different time scales and for different intrinsic modes functions. All measured TDICs have highlighted strong correlations between O 3 and meteorological parameters. The impact of weather conditions (e.g., drought, rainfall, windy period) have been locally observed on O 3 formation for all the time scales. This study shows that TDIC method is a powerful tool for perform multi-scale cross-correlation analysis in air pollution field. • First application of TDIC method in air pollution field. • Scaling properties of O 3 and climate parameters was highlighted in Caribbean region. • Multi-scale decomposition was performed on time series. • Investigate local correlations between climate parameters and O 3 concentrations. [ABSTRACT FROM AUTHOR]

Health effects from long- and short-term exposure to fine particulate matter (PM 2.5) have resulted in an annual average PM 2.5 standard across Europe and World Health Organisation guidelines for annual (10 μg m−3) and 24 h PM 2.5 concentrations (25 μg m−3). Developing strategies to reduce both annual and 24 h average PM 2.5 requires that the conditions that produce the magnitude of these metrics are understood. This paper presents a standard and replicable set of statistics that link the magnitude of annual and daily PM 2.5 metrics to variation in i) hourly PM 2.5 concentrations, ii) geographic regions traversed by air mass back trajectories, and iii) the 'urban increment' and 'regional contribution' to urban PM 2.5 concentrations. These statistics are calculated between 2009 and 2018 at monitoring sites across Paris and the Île-de-France region, France, where there is a national objective to achieve the WHO annual PM 2.5 guideline, and where short-term PM 2.5 episodes still occur. The aim is to investigate changes in the conditions producing annual average, and 24 h PM 2.5 concentrations exceeding 25 μg m−3, and how these long- and short-term metrics could be reduced further. The statistics indicate that reductions between 2009 and 2018 in both annual PM 2.5 concentrations (PM 2.5AA , −0.79 μg m−3 y−1 averaged across 3 urban background sites (33% average 2009–2018 reduction)) and the number of days with 24 h PM 2.5 concentrations above 25 μg m−3 (D24h25, -6 days y−1 (62% average 2009–2018 reduction)), were driven by reductions in local emissions in Paris and the Île-de-France region. For example, reduction in PM 2.5AA and D24h25 were greater at urban traffic sites, and between 2009 and 2018 the highest hourly PM 2.5 concentrations occurred less frequently during rush hour periods, while the lowest hourly PM 2.5 concentrations occurred more frequently during the day. In addition, when relatively moderate and high hourly PM 2.5 concentrations were measured, air mass back trajectories spent more time (during the 4 preceding days) over European geographic regions, compared to the ocean indicating an increased relative contribution from regional transport to these hourly PM 2.5 concentrations. Consequently, there is now a greater difference in the contribution of different hourly PM 2.5 concentrations to annual and 24 h PM 2.5 compared with 2009, with relatively high hourly PM 2.5 concentrations having a larger contribution to D24h25, and moderate hourly PM 2.5 concentrations having a larger contribution to PM 2.5AA. Strategies to reduce PM 2.5 concentrations in Paris should consider how mitigation measures will affect different ranges of hourly PM 2.5 concentrations to understand the (potentially differing) effect on long- and short-term PM 2.5 impact metrics. Comparison of hourly PM 2.5 concentrations at urban sites and upwind rural sites showed regional contributions to PM 2.5AA of approximately 50% and 70% at urban traffic and urban background sites, respectively. The largest regional contributions were also estimated for the highest hourly PM 2.5 concentrations, compared to moderate hourly PM 2.5 concentrations. Regional emission reductions could therefore make a substantial contribution to achieving the WHO air quality guidelines in Paris. • Changes in fine particulate matter measured in Paris assessed between 2009 and 2018. • Changes in annual and 24 h PM 2.5 metrics linked to changes in hourly PM 2.5 • Reduction in annual and 24 h PM 2.5 driven by local emission reductions. • Large regional contribution to PM 2.5 at urban background and traffic sites calculated. [ABSTRACT FROM AUTHOR]

The air pollution in Hotan, Xinjiang is serious and the high concentration of polycyclic aromatic hydrocarbons (PAHs) cannot be ignored. Studies on the characteristics of PAHs in the region are still limited. In this paper, PM 10 and PM 2.5 samples were collected from the urban area of Hotan in summer and winter, and the concentrations, sources and contributions to hydroxyl radicals (·OH) of 12 PAHs were analyzed. The mean concentrations of PM 2.5 /PM 10 -bound PAHs were much higher in winter (150 ± 38.8 ng/m3 and 156 ± 38.1 ng/m3) than in summer (6.34 ± 6.28 ng/m3 and 7.64 ± 6.75 ng/m3), with greater contributions from medium and high molecular weight PAHs. PAHs are mainly derived from vehicle emissions, coal and biomass combustion, with coal combustion making the largest contribution to ·OH concentrations. The combination of air mass trajectory clustering and potential source contribution factor analysis (PSCF) revealed that long-range transport contributed less to the PAHs content, and local domestic source emissions dominated. Studies have shown a significant positive correlation between PAHs and ·OH concentrations, indicating that PAHs are oxidatively toxic to humans. Controlling PAHs emission in winter in Hotan and reducing the concentration of PAHs in respirable particulate matter will help to reduce the damage to human health. • PM 2.5 /PM 10 –bound PAHs in urban areas of Hotan City were studied in winter and summer. • PAHs mainly coalesced in fine particulate matter (PM 2.5), not PM 2.5-10. • PAHs were emitted from local mixed sources, with coal combustion and vehicle emissions being the main sources. • MMW-PAHs and HMW-PAHs showed a good positive correlation with ·OH concentration. [ABSTRACT FROM AUTHOR]

Abstract: Pesticide spray drift entails a series of risks and costs in terms of human, animal and environmental well-being. A proper understanding of this phenomenon is essential to minimise these risks. However, most conventional methods used in drift measurement are based on point collectors which are unable to obtain information concerning the temporal or spatial evolution of the pesticide cloud. Such methods are also costly, labour-intensive, and require a considerable amount of time. The aim of this paper is to propose a method to measure the spray drift based on lidar (LIght Detection And Ranging) and to prove that it can be an alternative to passive collectors. An analytical model is proposed to relate the measurements obtained through passive collectors and those obtained with lidar systems considering several spray application and meteorological parameters. The model was tested through an experimental campaign involving multiple ground spray tests. A lidar system and two types of passive collectors (nylon strings and water-sensitive paper) were used simultaneously to measure the drift. The results showed for each test a high coefficient of determination (R 2 ≈ 0.90) between the lidar signal and the tracer mass captured by the nylon strings. This coefficient decreased (R 2 = 0.77) when all tests were considered together. Lidar measurements were also used to study the evolution of the pesticide cloud with high range (1.5 m) and temporal resolution (1 s) and to estimate its velocity. Furthermore, a very satisfactory adjustment (R 2 = 0.89) was observed between the tracer mass collected by the nylon lines and the coverage on water-sensitive paper sheets. These results are in accordance with the proposed analytical model and allow the conclusion that the application and meteorological parameters can be considered spatially invariant for a given test but are not invariant for different tests. [Copyright &y& Elsevier]

Abstract: This paper introduces an initiative to bridge the state of scientific knowledge on air pollution with the needs of policymakers and stakeholders to design the “next generation” of air quality indicators. As a first step this initiative assesses current monitoring and modeling associated with a number of important pollutants with an eye toward identifying knowledge gaps and scientific needs that are a barrier to reducing air pollution impacts on human and ecosystem health across the globe. Four outdoor air pollutants were considered – particulate matter, ozone, mercury, and Persistent Organic Pollutants (POPs) – because of their clear adverse impacts on human and ecosystem health and because of the availability of baseline data for assessment for each. While other papers appearing in this issue will address each pollutant separately, this paper serves as a summary of the initiative and presents recommendations for needed investments to provide improved measurement, monitoring, and modeling data for policy-relevant indicators. The ultimate goal of this effort is to enable enhanced public policy responses to air pollution by linking improved data and measurement methods to decision-making through the development of indicators that can allow policymakers to better understand the impacts of air pollution and, along with source attribution based on modeling and measurements, facilitate improved policies to solve it. The development of indicators represents a crucial next step in this process. [Copyright &y& Elsevier]

Abstract: Heavy metals constitute an important group of persistent toxic pollutants occurring in ambient air and other media. One of the suspected sources of these metals in the atmosphere is combustion of transport fuels in road vehicles. However, estimates of the emissions of these metals from road vehicles as reported in national emission inventories show a very high variability in emission factors used. This paper provides high quality data on concentrations of heavy metals in fuels and derives default emission factors from these. The paper discusses these values against the emission estimates presently reported by the Parties to the LRTAP Convention. The measured concentrations of heavy metals in petrol and diesel fuel show a high variability between different samples taken at gas stations throughout Europe. Metal concentrations in road transport fuels vary over two orders of magnitude, but all remain in the ppb region (a few tenths of a ppb to a few hundred ppb for all metals). The frequency distributions of the measurements could be approximated by lognormal distributions. The emission factors, including 95 percent confidence intervals were derived from a statistical analysis of the survey data. We could not detect a significant difference between samples from different countries. The fuel based emission factors as derived in this study are complemented with those related to unintentional lubricant oil combustion. This allowed an estimation of total exhaust heavy metal emissions for UNECE Europe, indicating that As, Hg and Se exhaust emissions were dominated by fuel combustion while Cd, Cr, Cu, Ni, Pb, and Zn exhaust emissions were dominated by lubricant oil combustion. The proposed emission factors were generally lower than previously published emission factors. National emissions of heavy metals from vehicle exhaust, estimated in this study are in many cases considerably lower than those reported by the countries for this source. [Copyright &y& Elsevier]

Abstract: Every year during the winter and pre-monsoon periods most of the Indian peninsula, Bay of Bengal and the adjoining Indian Ocean are affected by a polluted regional haze, known as the South Asian Winter Haze (SAWH). This article is the first of a series of three papers collectively anticipated at the central idea of retrieving aerosol component type and effective radius of the SAWH by incorporating experimental data of ground-based solar radiometer into Mie theory, and validation by collocated in-situ measurements. The work presented in this paper provides an approach to retrieve optical depths and their spectral curvature for SAWH at a tropical coastal station, Visakhapatnam (17.7°N, 83.3°E). A dataset comprised of 92 wintertime atmospheric optical depths are obtained from spectral extinction measurements of direct solar flux by means of a Multi-Wavelength Solar Radiometer during the period of October 2002 to February 2004. The inter-channel correlations established a linear relation and the airmass conditions specific to this dataset suggested a two-component linear model; the stable and variable components of which are analysed to obtain the mentioned optical parameters. Results indicate that the spectral curvature is best represented by a Lorentz model with a central wavelength of 0.40425±0.02945μm, a width of 0.40382±0.10253μm, a constant of 0.92623±0.33793, and a wavelength independent component of 0.95778±0.15678. Resonance-like structure of the obtained spectral curvature resembles wavelength dependent extinction characteristic of absorbing soot particles; thereby supports the key findings from INDOEX campaign. [Copyright &y& Elsevier]

Abstract: Using the data on all live births (∼400,000) and criteria pollutants from the Chicago Metropolitan Statistical Area (MSA) between 2000 and 2004, this paper empirically demonstrates how mismatches in the spatiotemporal scales of health and air pollution data can result in inconsistency and uncertainty in the linkages between air pollution and birth outcomes. This paper suggests that the risks of low birth weight associated with air pollution exposure changes significantly as the distance interval (around the monitoring stations) used for exposure estimation changes. For example, when the analysis was restricted within 3 miles distance of the monitoring stations the odds of LBW (births <2500 g) increased by a factor of 1.045 (±0.0285 95% CI) with a unit increase in the average daily exposure to PM10 (in μg m−3) during the gestation period; the value dropped to 1.028 when the analysis was restricted within 6 miles distance of air pollution monitoring stations. The effect of PM10 exposure on LBW became null when controlled for confounders. But PM2.5 exposure showed a significant association with low birth weight when controlled for confounders. These results must be interpreted with caution, because the distance to monitoring station does not influence the risks of adverse birth outcomes, but uncertainty in exposure increases with the increase in distance from the monitoring stations, especially for coarse particles such as PM10 that settle with gravity within short distance and time interval. The results of this paper have important implications for the research design of environmental epidemiological studies, and the way air pollution (and potentially other environmental) and health data are collocated to compute exposure. While this paper challenges the findings of pervious epidemiological studies that have relied on coarse resolution air pollution data (such as county level aggregated data), the paper also calls for time-space resolved estimate of air pollution to minimize uncertainty in exposure estimation. [Copyright &y& Elsevier]

Abstract Despite that Heavy-Duty Vehicles (HDVs) represent a small part of the overall vehicle population they have been identified as one of the most important contributors to air pollution. This is one of the reasons why HDV emissions regulations are becoming more and more stringent worldwide. Following this trend, Europe introduced the Euro VI standard which includes more stringent emission limits for hydrocarbons, PM and NO x , while for the first time a limit for solid PN emissions was set. At the same time increased concern regarding greenhouse gas emissions led to a series of initiatives including HDVs CO 2 certification and monitoring with the latest being the submission of a proposal for CO 2 emission targets for 2025 and 2030. Despite the general concern, published data regarding Euro VI HDV emissions are scarce, while real-world emission factor measurements of regulated and unregulated pollutants are even more difficult to find. The main objective of this paper is to present real-world diesel Euro VI HDVs emissions of both gaseous pollutants and solid PN. For that reason five HDVs, including four trucks and one bus, were tested on-road under typical driving conditions. A breakdown of the emissions to low, medium, and high speed conditions was also performed with the aim investigating the performance of aftertreatment systems under different speed conditions. All tested vehicles performed better compared to older technology diesel HDVs, thus reflecting the technological improvements introduced over the last years. However, relatively high emissions were observed for some of the pollutants over low speed phases due to reduced effectiveness of the corresponding emission control systems. Calculated emission factors were also compared to existing emission inventories and good correlation was found for NO x , CO 2 and solid PN emissions. Highlights • Five Euro VI HDVs were tested on-road under typical driving conditions. • The vehicles demonstrated overall lower emissions compared to older technology HDVs. • Relatively high emissions were observed for some pollutants over low speed phases. • Emission inventories succeed in calculating real world EFs of Euro VI HDVs. [ABSTRACT FROM AUTHOR]

Abstract Urban parks have long been considered places of refuge from the bustle, noise and pollutants of the surrounding city. Parks were labelled the lungs of the city, a metaphor which gained support as studies revealed the deposition of pollutants to leaf surfaces. More recently air purification by urban vegetation came to be seen as an ecosystem service, that add to the other important services it provides. However, the significance of deposition to foliage in reducing air pollutant concentrations in cities and the service this provides is often questioned. Although pollutants deposit on vegetation, the change in turbulence and wind speed brought about by increased friction can often limit dispersion and means that pollutant concentrations can be higher than expected. This paper re-analyses pollutant transects away from roads and into parks to compare the relevance of deposition and dispersion to lowered concentrations in park interiors and shows that pollutant concentrations typically decline more rapidly along transects with vegetation than those without. This may support the notion that deposition to vegetation rapidly reduces pollutant concentrations, but simulations using ENVI-MET reveal that even dense foliage offers little improvement to air quality via removal of pollutants in small urban parks with length scale less than 100 m. New field observations show the dominance of aerodynamic factors in changing air quality in urban parks and the way vegetation affects pollutant concentrations through altering air flows. Deposition is generally not important for parks although it plays a role in the rapid removal of large particles with rapid settling velocities (>10 cm s−1) at the park borders (<5 m). Across cities as a whole rarely more than a percent or so of individual pollutants are removed by urban vegetation. Deposition on foliage is not an important contribution to improved air quality. Vegetation can alter the dispersion of pollutants from roads into parks and effectively increase pollutant concentrations. Nevertheless stands of trees or flower beds can affect the distribution of park users, so may change exposure to air pollutants by altering the spatial distribution of occupants. Graphical abstract Image 1 Highlights • Pollutant deposition to vegetation plays little role in improving park air quality. • Suppression of dispersion by vegetation increases pollutant concentrations in parks. • Pollutant decrease from road to park core largely arises from dispersion. • Vegetation layout can encourage parks users to avoid polluted boundaries. • Only a minimal air purification service is provided by small urban parks. [ABSTRACT FROM AUTHOR]

Abstract This paper presents the results of research on the impact of various projected scenarios for urban bus transportation modernization in Krakow, in regards to air emissions. The research was carried out on the fleet of urban buses operating within the Krakow metropolitan area. The levels and amounts of the 16 substances were evaluated using the CORINAIR methodology. The scenarios were determined based on the data measured between 2010 and 2015 and according to the plans created by Krakow's carriers. An analysis of the plans for the urban bus fleet modernization in Krakow shows that it will have a positive influence on the reduction in particulate matter and gas emissions into the air. The implementation of the projected scenarios, which involves the replacement of old buses with modern, low or zero emission vehicles, will reduce the emission of nitrogen oxides (NO x) by more than 60%, volatile organic compounds (VOCs) by more than 82%, carbon monoxide (CO) by nearly 52%, and particulate matter from fuel combustion (PM ex) by more than 77% by 2025. Research also shows an increase N 2 O emissions (by almost 43%) as a result of modernization. This phenomenon is characteristic for modern engines. Moreover, introducing electric buses into a fleet contributes to the gradual reduction of benzo(a)pyrene emissions. Even though the replacement of a bus fleet is a long-term and costly process, taking such action for Krakow and the neighboring municipalities is essential to improving the air quality in that area, especially in terms of excessive concentrations of particulate matter and nitrogen oxides. Highlights • Modernization of urban buses will help to reduce road transportation emissions into the air. • The research allowed for an assessment of the emission reduction from urban buses in the analyzed municipalities. • The undertaken actions of replacing the urban bus fleet will improve the quality of Krakow's air. [ABSTRACT FROM AUTHOR]

Abstract Light rail transit (LRT) has a reputation for being cleaner and healthier than automobiles. However, few studies have examined the effects of mode shift from automobile to LRT. This paper investigates to what extent mode shift from car to LRT can reduce personal exposure to PM 2.5 , BC, and UFP. Simultaneous measurements on LRT and automobile were conducted under four plausible commuting scenarios in Los Angeles, California from October to November in 2014. As a robustness check, Monte Carlo simulations were performed to test the effects of confounding factors. Under the closed ventilation and low traffic condition, personal exposure measurements inside LRT were significantly higher than in automobile for BC (+40%, p < 0.01) and UFP (+27%, p < 0.01). However, under the open ventilation and high traffic condition, personal exposure was significantly lower in LRT than in automobile for PM 2.5 (−38%, p < 0.01); BC (−68%, p < 0.01); and UFP (−141%, p < 0.01). Results of the Monte Carlo simulations suggest that other factors, such as vehicle fan strength, vehicle speed, and vehicle age, appear to have marginal effects on personal exposure. These results demonstrate that the effect of mode shift from car to LRT can be significantly altered by ventilation settings and travel route choices. Results inform future research and policy about the importance of incorporating ventilation and traffic microenvironments for assessing the health effects of a mode switch from car to LRT. Graphical abstract Image 1 Highlights • Examined the effect of mode shift from car to LRT using simultaneous measurements. • Developed Monte Carlo simulations to test the effects of confounding factors. • The effect of mode shift was driven by ventilation and traffic condition. • Other factors, such as vehicle age, speed, and fan strength, were less influential. [ABSTRACT FROM AUTHOR]

Abstract: By means of computational fluid dynamic (CFD) simulations, this paper investigated how rural winds transport relatively clean air into high-rise urban areas to dilute airborne pollutants. Two ventilation concepts for indoor environments were applied: the age of air to quantify the time taken by rural young air to reach a given place after it enters an urban area and the air exchange efficiency to evaluate the efficiency of rural winds flushing the entire urban canopy layer. Several square building arrays with street aspect ratios (building height/street width, H/W) ranging from 1.5 to 5.3 and building area densities of 0.25 (medium) or 0.4 (compact) were considered as the approaching wind is parallel to the main street whose length varies from street scales to neighborhood scales (330–510 m to 1.03–1.65 km in full-scale). Results show that considerable young rural air enters windward entries but a major fraction of air is vertically driven out as flowing deeper into such high-rise building arrays. So air exchange efficiencies are less than 50% in street-scale arrays, and smaller in longer (neighborhood-scale) or narrower arrays. For the neighborhood-scale medium arrays, considering the power-law velocity profile in the upstream free flow, a taller array gains a larger inflow rate across its windward entry and experiences younger air and greater air exchange efficiency than a lower one. If all buildings are theoretically open-based in a neighborhood-scale compact array, air becomes much younger everywhere and the air exchange efficiency doubles. In arrays of buildings with different heights, the secondary streets in front of taller buildings get younger air due to the downward flows within them. Although further investigations are still required before providing a practical framework, this paper is one of the first attempts to find ways in improving the ventilation performance in high-rise cities like Hong Kong. [Copyright &y& Elsevier]

Abstract: This paper analyzes the mass size distribution of some inorganic species present in the atmospheric aerosol from a field campaign carried out in Madrid throughout a complete year (February 2007–February 2008). Samplings were performed by means of a micro-orifice uniform deposit impactor (MOUDI). Ambient air was sampled during consecutive nocturnal and diurnal periods, and diurnal/nocturnal behaviors were compared for the twenty night-day sampling pairs that were gathered. Annual and seasonal averages were obtained, and some case studies under specific atmospheric conditions are discussed in the paper. Results have shown that the sulfate and ammonium mass was concentrated in the accumulation mode, between 0.18 and 0.56μm, so that gas-phase and condensation processes for secondary aerosol formation prevailed during the sampling periods in this area. An exception to this behavior was found during a fog event when distributions for these two species were centered in the 0.56–1 and 1–1.8μm size stages, corresponding to the droplet mode. In most of the samples, the ammonium mass measured in these size ranges was enough or almost enough to neutralize inorganic acidity by formation of ammonium sulfate and nitrate. However, a significant sulfate mass not neutralized by ammonium was found in the impactor backup quartz filter (aerodynamic diameter<0.056μm). The concentration of this sulfate and its contribution to the ultrafine fraction mass was higher under good dispersive conditions, prevailing in summer, when particle growth processes are not so favored due to the higher atmospheric dilution factors. The origin of this ultrafine sulfate has been attributed to direct emissions from traffic, associated to the nucleation mode. Regarding the nitrate concentration, it was found higher in the coarse mode than in the accumulation mode on an annual basis. The highest concentrations were measured in winter episodic situations. The marked seasonal variability shown in the accumulation mode by this species is related to the volatility of ammonium nitrate. Among all the studied ions, nitrate also presented the highest differences between the diurnal and nocturnal concentrations in all size ranges. This is probably related to the short time scale required for its formation from gaseous precursors. [Copyright &y& Elsevier]

Abstract: This paper presents a decision-making framework (DMF) for selecting ozone control strategies based on reductions of precursor emissions that are targeted by time and location. Conventional across-the-board reductions are uniform throughout the region and throughout the day. The DMF is comprised of four phases: (1) Initialization, (2) Experimental Design and Data Mining, (3) Metamodeling, and (4) Optimization. This paper details the first three and presents an example for selecting strategies in the optimization phase. Specifically, to enable an efficient optimization phase, metamodels for maximum 8-h averaged ozone are employed to represent the output from an advanced photochemical model, such as CAMx. The process for each phase is presented for a Dallas Fort Worth (DFW) 2009 future emission scenario, based on a 10-day meteorological episode from August 13–22, 1999. Over 600 emission variables were identified in three source categories viz. point, area (includes non-road) and line (on-road). The DFW application demonstrates how control measures can be efficiently explored in a targeted manner instead of using the conventional “trial and error” approach. [Copyright &y& Elsevier]

Abstract: Exposure to tobacco pollutants that linger indoors after smoking has taken place (thirdhand smoke, THS) can occur over extended periods and is modulated by chemical processes involving atmospheric reactive species. This study investigates the role of ozone and indoor surfaces in chemical transformations of tobacco smoke residues. Gas and particle constituents of secondhand smoke (SHS) as well as sorbed SHS on chamber internal walls and model materials (cotton, paper, and gypsum wallboard) were characterized during aging. After smoldering 10 cigarettes in a 24-m3 room size chamber, gas-phase nicotine was rapidly removed by sorption to chamber surfaces, and subsequently re-emitted during ventilation with clean air to a level of ∼10% that during the smoking phase. During chamber ventilation in the presence of ozone (180ppb), ozone decayed at a rate of 5.6h−1 and coincided with a factor of 5 less nicotine sorbed to wallboard. In the presence of ozone, no gas phase nicotine was detected as a result of re-emission, and higher concentrations of nicotine oxidation products were observed than when ventilation was performed with ozone-free air. Analysis of the model surfaces showed that heterogeneous nicotine-ozone reaction was faster on paper than cotton, and both were faster than on wallboard. However, wallboard played a dominant role in ozone-initiated reaction in the chamber due to its large total geometric surface area and sink potential compared to the other substrates. This study is the first to show in a room-sized environmental chamber that the heterogeneous ozone chemistry of sorbed nicotine generates THS constituents of concern, as observed previously in bench-top studies. In addition to the main oxidation products (cotinine, myosmine and N-methyl formamide), nicotine-1-oxide was detected for the first time. [Copyright &y& Elsevier]

Abstract: This paper uses the 2006 IPCC Guidelines and latest country specific emission factors to estimate Indian methane emissions at sectoral and district level for the years 1990, 1995, 2005 and 2008. The estimates show that while methane emissions have increased steadily over past two decades, their share in India’s aggregate GHG emissions has declined from 31% in 1985 to 27% in 2008 mainly due to relatively higher growth CO2 emissions from the fossil fuels. The estimates for the year 2008 show that: i) agriculture sector, which employed two-thirds of India’s population and contributed 17% of GDP, accounted for 23% of India’s GHG emissions ii) 83% of country’s methane emissions are contributed by enteric fermentation, manure use and rice production, and iii) methane emissions from urban solid waste are steadily rising over the past two decades; their share in aggregate methane emissions has reached 8%. Resting on the disaggregated emissions estimates, the paper argues for using geographical and sectoral flexibilities to develop a roadmap for mitigation of methane emissions for India. [Copyright &y& Elsevier]

Abstract: This paper investigates numerical method for predicting wind field and pollutant dispersion in an urban area with crowded buildings and heavy traffic. Large eddy simulation is employed in numerical computation with Lagrangian dynamic model to close the subgrid scale stress. The fine grids are required in numerical simulation of micro-atmospheric flows in an urban area with crowded and complex building-streets. To realize the numerical simulation on available computer resources a domain decomposition method and a combined model for the building-street layout are proposed that the computed domain of an urban area is decomposed into a central area, where the pollution is the major concern and a surrounding region where the pollutant distribution is not concerned. The combined flow model is proposed for the complex building-street layout that the fine grid mesh and exact flow boundary conditions are prescribed in the central area while the buildings are treated as roughness elements in the surrounding region where the coarse grids are used. The proposed numerical method is applied to simulate the wind field and pollutant dispersion from vehicle exhaust in the Rua Do Campo area of Macau (referred to the site hereafter). The wind speed and temperature fields are computed from 7 am on September 6 2005 to 7 am of the next day and the pollutant concentration field is computed from 7 am to 6 pm on September 6 2005. The results show fairly good agreement with the field observation. The wind field and pollutant concentration distribution are demonstrated in the site by proposed combined model and they are compared with those computed by drag element model in whole computational domain. The results show that the combined model is an appropriate method for predicting wind field and pollutant dispersion in the crowded building area. The paper also reveals the relevance of buoyancy effect in the prediction of urban atmospheric environment. [Copyright &y& Elsevier]

Abstract: Maritime transport plays a central role in the transport sector’s sustainability debate. Its contribution to air pollution and greenhouse gases is significant. An effective policy strategy to regulate air emissions requires their robust estimation in terms of quantification and location. This paper provides a critical analysis of the ship emission modelling approaches and data sources available, identifying their limits and constraints. It classifies the main methodologies on the basis of the approach followed (bottom-up or top-down) for the evaluation and geographic characterisation of emissions. The analysis highlights the uncertainty of results from the different methods. This is mainly due to the level of uncertainty connected with the sources of information that are used as inputs to the different studies. This paper describes the sources of the information required for these analyses, paying particular attention to AIS data and to the possible problems associated with their use. One way of reducing the overall uncertainty in the results could be the simultaneous use of different sources of information. This paper presents an alternative methodology based on this approach. As a final remark, it can be expected that new approaches to the problem together with more reliable data sources over the coming years could give more impetus to the debate on the global impact of maritime traffic on the environment that, currently, has only reached agreement via the “consensus” estimates provided by . [Copyright &y& Elsevier]

Abstract Locating and quantifying the hazardous material source plays a significant role in the emergency action plans related to the intentional or accidental release. This issue is the most important in the cities where the threat to peoples health is the highest. In this paper, we present a complete framework to identify in real-time the airborne toxics source in an urban environment. Proposed framework utilizes the adjusted sequential version of the Approximate Bayesian Computation (ABC) methodology. Dedicated modifications enhanced the estimation of the posterior distributions of contamination source parameters significantly. The source parameters assessments are dynamically updated with the use of on-line arriving concentrations of released substance reported by sensors network. In reconstruction we utilize the advanced Quick Urban & Industrial Complex Dispersion Modeling system (QUIC), developed by Los Alamos National Laboratory, to predict the concentrations at the sensors locations. We validate the proposed methodology on real data coming from a full-scale field experiment DAPPLE conducted in central London in 2007. We confirm the effectivity of the proposed framework by successful estimation of six parameters characterizing the contamination source, i.e., source position (x,y,z) in a city environment, the mass of release (q), the release start time (s) and its duration (l). The presented results prove the utility of the proposed approach for event reconstruction problem in the highly urbanized environment. The presented framework can be adopted quickly during the emergency response planning in any complex urban environment with the use of suitable dispersion model. Graphical abstract Image 1 Highlights • Sensors data-driven localization of atmospheric contamination source in urban area. • SMC ABC algorithm modifications dedicated to source identification task in urban area. • Estimation of a posteriori distribution of airborne toxics source parameters. • Verification of the proposed framework with use of the data from DAPPLE experiment. • Successful stochastic reconstruction of six source parameters in urban area. [ABSTRACT FROM AUTHOR]

During the past decades, many authors have studied extreme ozone ( O 3 ) events on successive days in several cities around the world, where extreme pollution concentrations are considered as values exceeding air quality standards. These multiday episodes are caused by different variables: weather conditions, pollution precursors life times and air pollution transport. However, a complete characterization of the temporal behavior of multiday extreme O 3 episodes is still lacking. In the present paper, we used the Haar wavelet transform to study the period ( T in days) of multiday extreme O 3 episodes in Mexico city during 2015–2016, when 10 ozone contingencies occurred and changes in driving restrictions were implemented. In addition, we studied the temporal correlations between extreme O 3 and extreme: nitrogen dioxide ( NO 2 ), carbon monoxide (CO) and ultraviolet B radiation (UVB) for a broad range of time scales by means of the Haar Wavelet cross-correlation method. The results show that multiday O 3 episodes mainly exhibit periods of T > 4 days, while NO 2 and CO show multiday episodes comprising principally periods of T > 2 days. The cross correlation analysis reveals that CO and NO 2 are temporal anti-correlated with O 3 for daily variations T < 1 . However, NO 2 and CO are strongly and moderately correlated with O 3 for T > 4 , respectively, indicating that NO 2 , CO and O 3 are correlated in a multi-temporal clustered form. [ABSTRACT FROM AUTHOR]

The wildfires in Fort McMurray of Alberta, Canada, injected large amounts of smoke aerosols in May 2016 and were identified as being one of Canada's major weather events of the year. This paper presents a synergistic remote sensing and in-situ measurement of the resultant smoke plume transport, optical properties, and its impacts on local air quality in New York City (NYC). Comparisons with the operational air quality model forecast (the NOAA National Air Quality Forecasting Capability, NAQFC) performance are presented. The aloft plume intrusions on May 9–13 and 25–29, 2016, and their mixing down into the planetary-boundary layer (PBL) were observed by a combined lidar, ceilometer and other measurement. A decrease in single-scattering albedo and absorption Angstrom exponent near one indicates that the plumes were absorbing aerosol dominated. Dramatic impacts of smoke transport on the ground air quality are demonstrated with a coincident increase of ground PM 2.5 (from 5- to 25–30 μg/m 3 ) in NYC urban and upwind rural area, enhancement of the PM 2.5 speciation (organic carbon, elemental carbon, potassium ion (K + )) and the ozone exceedance of NAAQS. Using the satellite and model product, we show regional spatial distribution of smoke, multiple transport paths and wildfire sources. Finally, with the lidar vertical profiling observations, we evaluate the model PBL-height (PBLH) and PM 2.5 during May 24th to 30th, 2016. The model PBLH shows consistent diurnal variation with the observed mixing layer height (MLH), but is clearly overestimated during the convective daytime hours. On the other hand, when estimating the MLH directly from the model PM 2.5 profile, better agreement with observation was indicated. This helps explain the good agreement between the model PM 2.5 and surface measurements except for the model overestimate during the morning of May 25 and 26, 2016. [ABSTRACT FROM AUTHOR]

Evidence of combined mortality effects of multi-pollutant on cardiovascular diseases (CVD) and the corresponding exposure-response (ER) relationship is limited. In this paper, we examined the association between four ambient air pollutants (i.e., fine particulate matter, PM 2.5 , particulate matter, PM 10 , nitrogen dioxide, NO 2 , and sulfur dioxide, SO 2 .) and CVD mortality and the corresponding ER relationship after incorporating the potential interaction among the multiple pollutants. Bayesian kernel machine regression (BKMR) was used to evaluate the ER relationship and to explore the interactions between pollutants. The results showed that PM 10 and SO 2 were dominant pollutants from 0 to 2 days, while PM 2.5 and NO 2 had strong effect on CVD mortality from 3 to 4 days. Generally, PM 2.5 and NO 2 had the similar ER relationship across different moving average concerning the CVD mortality. For the interaction among the multiple pollutants, we found that there is no interaction between particle pollutants (i.e. PM 2.5 and PM 10 ) and gaseous pollutants (i.e.NO 2 and SO 2 ). On the contrary, there might be an interaction between PM 2.5 and PM 10 though this interaction was detected by visually comparing the slopes of ER curves of a given particle pollutant at different levels of the other particle pollutant. But there is a lack of statistical significance test for this interaction. This study suggests that different ambient air pollutants might have the dominant effect on CVD deaths during different moving average, though there might not be statistical significant interactions among the ambient air pollutants in present study. [ABSTRACT FROM AUTHOR]

Abstract: This paper is devoted to the study of pollutant concentration distribution within urban-like geometries. By applying efficiency concepts originally developed for indoor environments, the term ventilation is used as a measure of city “breathability”. It can be applied to analyse pollutant removal within a city in operational contexts. This implies the evaluation of the bulk flow balance over the city and of the mean age of air. The influence of building packing density on flow and pollutant removal is, therefore, evaluated using those quantities. Idealized cities of regular cubical buildings were created with packing density ranging from 6.25% to 69% to represent configurations from urban sprawl to compact cities. The relative simplicity of these arrangements allowed us to apply the Computational Fluid Dynamics (CFD) flow and dispersion simulations using the standard k–ɛ turbulence model. Results show that city breathability within the urban canopy layer is strongly dependent from the building packing density. At the lower packing densities, the city responds to the wind as an agglomeration of obstacles, at larger densities (from about 44%) the city itself responds as a single obstacle. With the exception of the lowest packing density, airflow enters the array through lateral sides and leaves throughout the street top and flow out downstream. The air entering through lateral sides increases with increasing packing density. At the street top of the windward side of compact building configurations, a large upward flow is observed. This vertical transport reduces over short distance to turn into a downward flow further downstream of the building array. These findings suggest a practical way of identifying city breathability. Even though the application of these results to real scenarios require further analyses the paper illustrates a practical framework to be adopted in the assessment of the optimum neighbourhood building layout to minimize pollution levels. [Copyright &y& Elsevier]

In this paper, a new method to calculate the average spatial distribution of air pollutants based on diffusive sampling measurements and artificial neural networks evaluation is presented. Most established methods like interpolation algorithms are inflexible or limited in considering important distribution parameters such as emission sources or land use. Of special interest are air quality measurements since they provide a direct view on the actual pollutant level. With diffusive samplers, the average concentration of many gaseous species over a large area can be determined simultaneously. During a project in Cyprus, NO2 diffusive samplers were exposed at 270 sites in six month-long campaigns throughout one year providing the database for the model described in this paper. A multilayer perceptron was trained with the NO2 measurement data and distribution parameters like population density and meteorological parameters using a 1×1km grid covering Cyprus. The best fit could be achieved with an emissions inventory including previously simulated concentration plumes and population density data as input nodes for the neural network, resulting in realistic maps of the annual average distribution of NO2 in Cyprus. [Copyright &y& Elsevier]

The dispersion formulation incorporated in the U.S. Environmental Protection Agency''s AERMOD regulatory dispersion model is used to estimate the contribution of traffic-generated emissions of select VOCs – benzene, 1,3-butadiene, toluene – to ambient air concentrations at downwind receptors ranging from 10-m to 100-m from the edge of a major highway in Raleigh, North Carolina. The contributions are computed using the following steps: 1) Evaluate dispersion model estimates with 10-min averaged NO data measured at 7 m and 17 m from the edge of the road during a field study conducted in August, 2006; this step determines the uncertainty in model estimates. 2) Use dispersion model estimates and their uncertainties, determined in step 1, to construct pseudo-observations. 3) Fit pseudo-observations to actual observations of VOC concentrations measured during five periods of the field study. This provides estimates of the contributions of traffic emissions to the VOC concentrations at the receptors located from 10 m to 100 m from the road. In addition, it provides estimates of emission factors and background concentrations of the VOCs, which are supported by independent estimates from motor vehicle emissions models and regional air quality measurements. The results presented in the paper demonstrate the suitability of the formulation in AERMOD for estimating concentrations associated with mobile source emissions near roadways. This paper also presents an evaluation of the key emissions and dispersion modeling inputs necessary for conducting assessments of local-scale impacts from traffic emissions. [Copyright &y& Elsevier]

Ozone interacts with plant tissue through distinct temporal processes. Sequentially, plants are exposed to ambient O3 that (1) moves through the leaf boundary layer, (2) is taken up into plant tissue primarily through stomata, and (3) undergoes chemical interaction within plant tissue, first by initiating alterations and then as part of plant detoxification and repair. In this paper, we discuss the linkage of the temporal variability of apoplastic ascorbate with the diurnal variability of defense mechanisms in plants and compare this variability with daily maximum O3 concentration and diurnal uptake and entry of O3 into the plant through stomata. We describe the quantitative evidence on temporal variability in concentration and uptake and find that the time incidence for maximum defense does not necessarily match diurnal patterns for maximum O3 concentration or maximum uptake. We suggest that the observed out-of-phase association of the diurnal patterns for the above three processes produces a nonlinear relationship that results in a greater response from the higher hourly average O3 concentrations than from the lower or mid-level values. The fact that these out-of-phase processes affect the relationship between O3 exposure/dose and vegetation effects ultimately impact the ability of flux-based indices to predict vegetation effects accurately for purposes of standard setting and critical levels. Based on the quantitative aspect of temporal variability identified in this paper, we suggest that the inclusion of a diurnal pattern for detoxification in effective flux-based models would improve the predictive characteristics of the models. While much of the current information has been obtained using high O3 exposures, future research results derived from laboratory biochemical experiments that use short but elevated O3 exposures should be combined with experimental results that use ambient-type exposures over longer periods of time. It is anticipated that improved understanding will come from future research focused on diurnal variability in plant defense mechanisms and their relationship to the diurnal variability in ambient O3 concentration and stomatal conductance. This should result in more reliable O3 exposure standards and critical levels. [Copyright &y& Elsevier]

The present paper examines the use of zinc isotopes as tracers of atmospheric sources and focuses on the potential fractionation of Zn isotopes through anthropogenic processes. In order to do so, Zn isotopic ratios are measured in enriched ores and airborne particles associated with pyrometallurgical activities of one of the major Pb–Zn refineries in France. Supporting the isotopic investigation, this paper also compares morphological and chemical characteristics of Zn particles collected on dry deposition plates (“environmental samples”) placed within a 5km radius of the smelter, with those of Zn particles collected inside the plant (“process samples”), i.e. dust collected from the main exhaust system of the plant. To ensure a constant isotopic “supply”, the refinery processed a specific set of ores during the sampling campaigns, as agreed with the executive staff of the plant. Enriched ores and dust produced by the successive Zn extraction steps show strong isotope fractionation (from −0.66 to +0.22‰) mainly related to evaporation processes within the blast furnaces. Dust from the main chimney displays a δ66Zn value of −0.67‰. Application of the Rayleigh equation to evaluate the fractionation factor associated with the Zn vapor produced after a free evaporation gives a range of α ore/vapor from 1.0004 to 1.0008. The dry deposits, collected on plates downwind of the refinery, display δ66Zn variations of up to +0.7‰. However, it is to be noted that between 190 and 1250m from the main chimney of the refinery, the dry deposits show a high level of large (>10μm) Zn, S, Fe and O bearing aggregates characterized by positive δ66Zn values (+0.02 to +0.19‰). These airborne particles probably derive from the re-suspension of slag heaps and local emissions from the working-units. In contrast, from 1720 to 4560m, the dry... [Copyright &y& Elsevier]

Abstract: This paper aims at presenting a validation of multi-pollutants over Europe with a focus on aerosols. Chemistry-Transport Models are now used for forecast and emission reduction studies not only for gas-phase species but also for aerosols. Comprehensive model-to-data comparisons are therefore required. We present in this paper a preliminary validation study of the Polyphemus system applied over Europe for 2001. The aerosol model is the SIze REsolved Aerosol Model (SIREAM). It is a sectional model that describes the temporal evolution of the size/composition distribution of atmospheric particles containing a mix of black carbon, mineral dust, inorganic species, and primary and secondary organics. In addition to a brief model description, we present an overview of the model validation. A comprehensive set of model-to-data statistics is computed with observational data extracted from three European databases (the EMEP, AirBase and BDQA databases). Model performance criteria are verified for ozone and particulate matter (PM) and its inorganic components. Comparisons of correlations and root mean square errors with those generated by other models run over Europe for 2001 indicate a good performance of the Polyphemus system. Modifications of the system configuration and parameterizations may have a significant impact on error statistics, which may question the robustness of such models. Because large differences exist between databases, the robustness of model-to-data error statistics is also investigated. [Copyright &y& Elsevier]

This paper proposes and validates a methodology for the quantification of the daily African PM load during dust outbreaks in southern Europe. The daily net dust load in PM10 attributable to an African episode in a given region can be obtained by subtracting the daily regional background (RB) level from the PM10 concentration value at an RB station. As demonstrated in this paper, the daily RB level can be obtained by applying a monthly moving 30th percentile to the PM10 time series at an RB station after a prior extraction of the data of the days with African dust transport. The daily PM10 RB levels obtained can be subtracted from the daily PM10 levels recorded at the same RB site only on days when the occurrence of African dust outbreaks was demonstrated, the difference being the daily net African dust load. It is thus possible to quantify the African dust contribution during an African PM event in southern Europe without the need for PM speciation. The validation of this methodology was performed by comparing the estimated net dust load during African dust outbreaks at three RB stations and the crustal load determined by chemical speciation of PM10 filters. The correlation (r 2>0.6) and the equivalence (correlation lines’ slopes ∼1) were significant in the three cases. [Copyright &y& Elsevier]

Abstract: This paper presents preliminary results from an ongoing air quality project at a rural super site in Athens, Ohio (39°18′N, 82°7′W). Athens is located in the heart of the Ohio River Valley region, which is characterized by a high number of coal-fired power plants, chemical plants, and manufacturing industries. Highly time-resolved gaseous elemental mercury (Hg0), reactive gaseous mercury (RGM), and particulate mercury (Hgp) data are reported in this paper. Mercury species are measured using an automated Tekran 2537A CVAFS analyzer and Tekran 1130 and 1135 speciation and particulate modules. Continuous mercury data are reported from 27 July 2004, to 30 July 2005. This study also measured wet deposited mercury; reported here are results from 3 May 2004, until 31 May 2005. The highest mercury deposition occurred during September 2004. This is due in part to a series of unusual weather events featuring transport predominantly from the north, northeast, and southeast. The effects of the remnants of hurricanes Frances and Ivan on air quality data are also apparent during this month. This paper also presents results of PM2.5, gaseous co-pollutant, and meteorological measurements. Results are compiled into daily and monthly averages to display diurnal and seasonal patterns. Low background concentrations with frequent pollution episodes make this rural site an excellent location to capture transport events into and out of the Ohio River Valley. [Copyright &y& Elsevier]

Abstract: Motor vehicles are a major source of air pollutant emissions in Kathmandu valley, Nepal. In-use vehicle emission limits were first introduced in Nepal in 1998 and updated in 2000. The emission regulations for gasoline vehicles limit CO emissions to 3–4.5% by volume and HC emissions to 1000 ppm for four-wheeled vehicles, and 7800ppm for two- and three- wheelers. Emission limits for LPG/CNG vehicles are 3% for CO and 1000ppm for HC. For diesel vehicles, smoke density must not exceed 65–75 HSU depending on the age of the vehicle. The Government operates a rudimentary inspection and maintenance (I/M) program based on an idle engine test, utilizing an exhaust gas analyzer (for gasoline/LPG/CNG vehicles) and an opacimeter for diesel vehicles. The I/M program is confined to four-wheeled vehicles and occasional three-wheelers. The inspections are required at least once a year and are conducted at designated vehicle testing stations. The I/M program is supplemented by roadside checks. This paper is based on the findings of an analysis of vehicle emissions test data for the period June 2000 to July 2002, covering some 45,000 data sets. Each data set includes information on vehicle type and ownership, the model year, and CO/HC test emission values. The analysis reported in this paper covers the characteristics and statistical distribution of emissions from gasoline-fuelled vehicles, including the impact of gross emitters. The effects of vehicle age, model year (with or without catalysts), usage, and ownership (private vs. public) on emissions of gasoline-fuelled vehicles are discussed. The findings for diesel vehicles have been reported earlier by Ale and Nagarkoti (2003b. Evaluation of Kathmandu valley inspection and maintenance program on diesel vehicles. Journal of the Institute of Engineering 3(1)). This study identifies the limitations of the current I/M program, given that it does not include 70% of the fleet consisting of two-wheelers and concludes with proposed changes to the I/M program to make it more effective. [Copyright &y& Elsevier]

Abstract: The paper reviews the current state of knowledge regarding global emissions of mercury and presents a new inventory of global emissions of mercury to the atmosphere from anthropogenic sources for the year 2000. The largest emissions of Hg to the global atmosphere occur from combustion of fossil fuels, mainly coal in utility, industrial, and residential boilers. As much as two-thirds of the total emission of ca. 2190ton of Hg emitted from all anthropogenic sources worldwide in 2000 came from combustion of fossil fuels. Emissions of Hg from coal combustion are between one and two orders of magnitude higher than emissions from oil combustion, depending on the country. Various industrial processes account for additional 30% of Hg emissions from anthropogenic sources worldwide in 2000. Major contribution to emissions from this source category comes from gold production using Hg technology. The Asian countries contributed about 54% to the global Hg emission from anthropogenic sources in 2000, followed by Africa (18%) and Europe, including the European part of Russia (11%). China heads the list of the 10 countries with highest Hg emissions from anthropogenic activities. With more than 600ton of Hg, China contributes about 28% to the global emissions of mercury. It is expected that future changes of Hg emissions from anthropogenic sources worldwide until the year 2020 should be within ±20% of the current estimates, although this assessment should be treated with great caution. Emission estimates for various continents presented in this paper were used to prepare global emission maps. These maps are presented in a companion paper (Wilson et al., 2005. Spatial distribution of global anthropogenic mercury atmospheric emissions. Atmospheric Environment, in this issue). [Copyright &y& Elsevier]

Abstract: Over the Guangzhou region, also called the Pearl River Delta (PRD) of Southern China, there are often high aerosol concentrations. The satellite (MODIS) data show that the aerosol optical depth is often higher than 0.6 in this area, which is known as Asian Brown Cloud region. In some extreme events, these high aerosol levels have caused very low visibilities. In this paper, we will show that on 2 November 2003, the daily average visibility is less than 2km (the instantaneous value is less than 200m), and the aerosol optical depth is greater than 1.2. We analyze the cause of this particular event. The analysis indicates that this event is related to the hurricane of “Melor 0319”. The hurricane is located at the northwest Philippines on 2 November 2003, and the Guangzhou region is outside the hurricane. The hurricane produces a strong descent motion in the lower troposphere, a weak surface wind speed, and a relatively low planetary boundary layer (PBL) in the Guangzhou region. As a result, the strong subsidence causes descending air motions to force the aerosol particles into a very shallow layer, and the weak horizontal wind acts to keep the aerosol particles inside the source region, producing very high aerosol concentrations and extremely low visibility in the Guangzhou region on 2 November 2003. This case analysis will be useful to better understand the causes of the poor air quality over Guangzhou, which is a first step for ultimately improving it in the future. For example, one can reduce emissions when synoptic conditions are expected to cause recurrences of the phenomenon discussed in the paper. [Copyright &y& Elsevier]