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Exposure to bioaerosol allergens such as pollen can cause exacerbations of allergenic airway disease (AAD) in sensitive populations, and thus cause serious public health problems. Assessing these health impacts by linking the airborne pollen levels, concentrations of respirable allergenic material, and human allergenic response under current and future climate conditions is a key step toward developing preventive and adaptive actions. To that end, a regional-scale pollen emission and transport modeling framework was developed that treats allergenic pollens as non-reactive tracers within the WRF/CMAQ air-quality modeling system. The Simulator of the Timing and Magnitude of Pollen Season (STaMPS) model was used to generate a daily pollen pool that can then be emitted into the atmosphere by wind. The STaMPS is driven by species-specific meteorological (temperature and/or precipitation) threshold conditions and is designed to be flexible with respect to its representation vegetation species and plant functional types (PFTs). The hourly pollen emission flux was parameterized by considering the pollen pool, friction velocity, and wind threshold values. The dry deposition velocity of each species of pollen was estimated based on pollen grain size and density. An evaluation of the pollen modeling framework was conducted for southern California for the period from March to June 2010. This period coincided with observations by the University of Southern California's Children's Health Study (CHS), which included O3, PM2.5, and pollen count, as well as measurements of exhaled nitric oxide in study participants. Two nesting domains with horizontal resolutions of 12 km and 4 km were constructed, and six representative allergenic pollen genera were included: birch tree, walnut tree, mulberry tree, olive tree, oak tree, and brome grasses. Under the current parameterization scheme, the modeling framework tends to underestimate walnut and peak oak pollen concentrations, and tends to overestimate grass pollen concentrations. The model shows reasonable agreement with observed birch, olive, and mulberry tree pollen concentrations. Sensitivity studies suggest that the estimation of the pollen pool is a major source of uncertainty for simulated pollen concentrations. Achieving agreement between emission modeling and observed pattern of pollen releases is the key for successful pollen concentration simulations. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols can exert an influence on meteorology and air quality through aerosol–radiation interaction (ARI) and aerosol–cloud interaction (ACI), and this two-way feedback has been studied by applying two-way coupled meteorology and air quality models. As one of the regions with the highest aerosol loading in the world, Asia has attracted many researchers to investigate the aerosol effects with several two-way coupled models (WRF-Chem, WRF-CMAQ, GRAPES-CUACE, WRF-NAQPMS, and GATOR-GCMOM) over the last decade. This paper attempts to offer a bibliographic analysis regarding the current status of applications of two-way coupled models in Asia, related research focuses, model performances, and the effects of ARI and/or ACI on meteorology and air quality. There were a total of 160 peer-reviewed articles published between 2010 and 2019 in Asia meeting the inclusion criteria, with more than 79 % of papers involving the WRF-Chem model. The number of relevant publications has an upward trend annually, and East Asia, India, and China, as well as the North China Plain are the most studied areas. The effects of ARI and both ARI and ACI induced by natural aerosols (particularly mineral dust) and anthropogenic aerosols (bulk aerosols, different chemical compositions, and aerosols from different sources) are widely investigated in Asia. Through the meta-analysis of surface meteorological and air quality variables simulated by two-way coupled models, the model performance affected by aerosol feedbacks depends on different variables, simulation time lengths, selection of two-way coupled models, and study areas. Future research perspectives with respect to the development, improvement, application, and evaluation of two-way coupled meteorology and air quality models are proposed. [ABSTRACT FROM AUTHOR]

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, this paper presents a harmonised dataset containing daily values of air quality, weather, emissions, livestock, and land and soil use in the years 2016–2021, for the Lombardy region. The daily scale is obtained by averaging hourly data and interpolating other variables. In fact, the pollutant data come from the European Environmental Agency and the Lombardy Regional Environment Protection Agency, weather and emissions data from the European Copernicus programme, livestock data from the Italian zootechnical registry, and land and soil use data from the CORINE Land Cover project. The resulting dataset is designed to be used as is by those using air quality data for research. [ABSTRACT FROM AUTHOR]

The article explores the evaluation and description of the global Community Atmosphere Model (CAM) version 4 for atmospheric chemistry in Community Earth System Model (CESM). It reports that the atmospheric chemistry is responsible for the distribution of non-carbon dioxide gases and aerosols which influence the climate and air quality. Details on topics including the offline meteorology, chemical mechanisms, and emissions are discussed.

In recent years, quantifying the impacts of detrimental air quality has become a global priority for researchers and policy makers. At present, the systems and methodologies supporting the collection and manipulation of this data are difficult to access. To support studies quantifying the interplay between common gaseous and particulate pollutants with meteorology and biological particles, this paper presents a comprehensive data-set containing daily air quality readings from the Automatic Urban and Rural Network, and pollen and weather data from Met Office monitoring stations, in the years 2016 to 2019 inclusive, for the United Kingdom. We describe (1) the sources from which the data were collected, (2) the methods used for the data cleaning process and (3) how issues related to missing values and sparse regional coverage were addressed. The resulting data-set is designed to be used 'as is' by those using air quality data for research; we also describe and provide open access to the methods used for curating the data to allow modification of or addition to the data-set. Measurement(s) meteorology • air quality • pollen Technology Type(s) digital curation Factor Type(s) temporal interval Sample Characteristic - Environment air Sample Characteristic - Location United Kingdom [ABSTRACT FROM AUTHOR]

Wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical–meteorological interactions that drive high-pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in western U.S. basins. Approximately 120 people participated, representing 50 institutions and five countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological–chemical linkages outlined here, nor to validate complex processes within coupled atmosphere–chemistry models. [ABSTRACT FROM AUTHOR]

Because the power industry makes a significant contribution to air pollution, a variety of air pollution control technologies have been adopted to reduce emissions of nitrogen oxides, sulfur dioxide, and particulate matters. However, the deployment of these technologies affects the operation of the power plants and of the power system. This paper first discusses the emissions of multiple pollutants by coal- and gas-fired generators equipped with different emission control devices. It then presents the formulation of an environmental power generation scheduling (EnPGS) model, which coordinates the operating cost and the emissions of these pollutants, including the emissions during the startup and shutdown processes. This model considers the air quality index and how this index is affected by the weather, and optimizes the spatial distribution of generation between regions. It also takes into account the operating characteristics of various emission control devices, such as the deactivation of selective catalytic reduction at low output, and the burn mode switching of combined cycle gas turbines. A case study covering several Chinese provinces demonstrates the potential effectiveness of the EnPGS at reducing multiple air pollutants. [ABSTRACT FROM AUTHOR]

The winters in megacity Delhi are harsh, smoggy, foggy, and highly polluted. The pollution levels are approximately two to three times those monitored in the summer months, and the severity is felt not only in the health department but also in the transportation department, with regular delays at airport operations and series of minor and major accidents across the road corridors. The impacts felt across the city are both manmade (due to the fuel burning) and natural (due to the meteorological setting), and it is hard to distinguish their respective proportions. Over the last decade, the city has gained from timely interventions to control pollution, and yet, the pollution levels are as bad as the previous year, especially for the fine particulates, the most harmful of the criteria pollutants, with a daily 2009 average of 80 to 100 μg/m. In this paper, the role of meteorology is studied using a Lagrangian model called Atmospheric Transport Modeling System in tracer mode to better understand the seasonality of pollution in Delhi. A clear conclusion is that irrespective of constant emissions over each month, the estimated tracer concentrations are invariably 40% to 80% higher in the winter months (November, December, and January) and 10% to 60% lower in the summer months (May, June, and July), when compared to annual average for that year. Along with monitoring and source apportionment studies, this paper presents a way to communicate complex physical characteristics of atmospheric modeling in simplistic manner and to further elaborate linkages between local meteorology and pollution. [ABSTRACT FROM AUTHOR]

Regional-scale air pollution models are routinely being used worldwide for research, forecasting air quality, and regulatory purposes. It is well recognized that there are both reducible (systematic) and irreducible (unsystematic) errors in the meteorology–atmospheric-chemistry modeling systems. The inherent (random) uncertainty stems from our inability to properly characterize stochastic variations in atmospheric dynamics and chemistry and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because stochastic variations are not being explicitly simulated in the current generation of regional-scale meteorology–air quality models, one should expect to find differences between the model estimates and corresponding observations. This paper presents an observation-based methodology to determine the expected errors from current-generation regional air quality models even when the model design, physics, chemistry, and numerical analysis, as well as its input data, were "perfect". To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8 h ozone time series are separated from the longer-term forcing using a simple recursive moving average filter. The inherent uncertainty attributable to the stochastic nature of the atmosphere is determined based on 30+ years of historical ozone time series data measured at various monitoring sites in the contiguous United States (CONUS). The results reveal that the expected root mean square error (RMSE) at the median and 95th percentile is about 2 and 5 ppb , respectively, even for perfect air quality models driven with perfect input data. Quantitative estimation of the limit to the model's accuracy will help in objectively assessing the current state of the science in regional air pollution models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to ambient measurements. [ABSTRACT FROM AUTHOR]

The urban areas of Bangladesh suffer from severe air quality problem especially in dry season (November-April) when the PM concentrations frequently rise to 7-8 times of the WHO guideline value. The Clean Air and Sustainable Environment (CASE) Project of the Department of Environment has deployed countrywide continuous air monitoring systems to regularly monitor the air quality of the urban areas of Bangladesh. In this paper hourly concentrations of PM10 and PM2.5 captured using ß-attenuation method from 2013 to April 2018 in six important cities located in different regions of the country were exhaustively analyzed. Statistical analyses, diurnal and seasonal trends, and polar plots of PM concentrations were examined. Long range sources were spotted by Concentration Weighted Trajectory (CWT) method, where the trajectories were calculated using HYSPLIT-4 model. The analyses identified cities in the middle of the country (Dhaka, Narayanganj, Gazipur) as the most polluted ones while the city to the northeast region (Sylhet) was the least polluted. Average PM10 concentrations at Dhaka, Chattogram, Narayanganj, Gazipur, Sylhet and Barisal stations in dry seasons (November-April) were found 238.7±155.4, 190.7±108.5, 303.6±161.4, 227.3±142.7, 151.7±105.0 and 170.7±108.4 µg m-3 respectively whereas those in wet seasons (May-October) were 75.0±51.6, 55.5±40.8, 102.4±84.4, 60.6±48.5, 52.7±38.3, and 54.4±41.6 µg m-3 respectively. Correlative study of diurnal variations in PM concentrations and meteorological parameters revealed that the congenial meteorology aided in developing higher concentrations of both PM10 and PM2.5 during nighttime. Sources located to the northwestern districts (Naogao, Rangpur, Bogura) were traced by the CWT method contributing to the air pollution in other regions of the country. Outside the boundary, sources in Nepal, and Delhi-NCR and Uttar Pradesh regions of India could have contributed to fine particles at the middle of the country. [ABSTRACT FROM AUTHOR]

This paper describes the Eulerian urban dispersion model EPISODE. EPISODE was developed to address a need for an urban air quality model in support of policy, planning, and air quality management in the Nordic, and, specifically, Norwegian setting. It can be used for the calculation of a variety of airborne pollutant concentrations, but we focus here on the implementation and application of the model for NO2 pollution. EPISODE consists of a Eulerian 3D grid model with embedded sub-grid dispersion models (e.g., a Gaussian plume model) for dispersion of pollution from line (i.e., roads) and point sources (e.g., chimney stacks). It considers the atmospheric processes advection, diffusion, and a NO2 photochemistry represented using the photostationary steady state approximation for NO2. EPISODE calculates hourly air concentrations representative of the grids and at receptor points. The latter allow EPISODE to estimate concentrations representative of the levels experienced by the population and to estimate their exposure. This methodological framework makes it suitable for simulating NO2 concentrations at fine scale resolution (< 100 m) in Nordic environments. The model can be run in an offline nested mode using output concentrations from a global or regional chemical transport model and forced by meteorology from an external numerical weather prediction model; but it also can be driven by meteorological observations. We give a full description of the overall model function as well as its individual components. We then present a case study for six Norwegian cities whereby we simulate NO2 pollution for the entire year of 2015. The model is evaluated against in-situ observations for the entire year and for specific episodes of enhanced pollution during winter. We evaluate the model performance using the FAIRMODE DELTA Tool that utilizes traditional statistical metrics, e.g., RMSE, Pearson correlation, R, and bias along with some specialised tests for air quality model evaluation. We find that EPISODE attains the DELTA Tool model quality objective in all of the stations we evaluate against. Further, the other statistical evaluations show adequate model performance, but that the model scores greatly improved correlations during winter and autumn compared to the summer. We attribute this to the use of the photostationary steady state scheme for NO2, which should perform best in the absence of local ozone photochemical production. Oslo does not comply with the NO2 annual limit set in the 2008/50/EC directive (AQD). NO2 pollution episodes with the highest NO2 concentrations, which lead to the occurrence of exceedances of the AQD hourly limit for NO2 occur primarily in the winter and autumn in Oslo, so this strongly supports the use of EPISODE in the application of these winter-time events. Overall, we conclude that the model is suitable for assessment of annual mean NO2 concentrations and also for the study of hourly NO2 concentrations in the Nordic winter and autumn environment. Further, we conclude that it is suitable for a range of policy applications that include: pollution episode analysis, evaluation of seasonal statistics, policy and planning support, and air quality management. Lastly, we identify a series of model developments specifically designed to address the limitations of the current model assumptions. Part 2 of this two-part paper discusses the CityChem extension to EPISODE, which includes a number of implementations such as a more comprehensive photochemical scheme suitable for describing more chemical species and a more diverse range of photochemical environments, and a more advanced treatment of the sub-grid dispersion. [ABSTRACT FROM AUTHOR]

The microprocessor without interlocked piped stages (MIPS) and LoongArch are reduced instruction set computing (RISC) processor architectures, which have advantages in terms of energy consumption and efficiency. There are few studies on the application of MIPS and LoongArch central processing units (CPUs) in geoscientific numerical models. In this study, the Loongson 3A4000 CPU platform with the MIPS64 architecture and the Loongson 3A6000 CPU platform with the LoongArch architecture were used to establish the runtime environment for the air quality modelling system Weather Research and Forecasting–Comprehensive Air Quality Model with extensions (WRF-CAMx) in the Beijing–Tianjin–Hebei region. The results show that the relative errors for the major species (NO 2 , SO 2 , O 3 , CO, PNO 3 , and PSO 4) between the MIPS and X86 benchmark platforms are within ± 0.1 %. The maximum mean absolute error (MAE) of major species ranged up to 10 -2 ppbV or µ g m -3 , the maximum root mean square error (RMSE) ranged up to 10 -1 ppbV or µ g m -3 , and the mean absolute percentage error (MAPE) remained within 0.5 %. The CAMx takes about 195 min on the Loongson 3A4000 CPU, 71 min on the Loongson 3A6000 CPU, and 66 min on the Intel Xeon E5-2697 v4 CPU, when simulating a 24 h case with four parallel processes using MPICH. As a result, the single-core computing capability of the Loongson 3A4000 CPU for the WRF-CAMx modelling system is about one-third of the Intel Xeon E5-2697 v4 CPU, and the one of Loongson 3A6000 CPU is slightly lower than that of Intel Xeon E5-2697 v4 CPU; but, the thermal design power (TDP) of Loongson 3A4000 is 40 W, while the TDP of Loongson 3A6000 is 38 W, only about one-fourth of that of Intel Xeon E5-2697 v4, whose TDP is 145 W. The results also verify the feasibility of cross-platform porting and the scientific usability of the ported model. This study provides a technical foundation for the porting and optimization of numerical models based on MIPS, LoongArch, or other RISC platforms. [ABSTRACT FROM AUTHOR]

The model performance of a regional-scale meteorology-chemistry model (NHM-Chem) has been evaluated for the consistent predictions of the chemical, physical, and optical properties of aerosols. These properties are essentially important for the accurate assessment of air quality and health hazards, contamination of land and ocean ecosystems, and regional climate changes due to aerosol-cloud-radiation interaction processes. Currently, three optional methods are available: the five-category non-equilibrium method, the three-category non-equilibrium method, and the bulk equilibrium method. These three methods are suitable for the predictions of regional climate, air quality, and operational forecasts, respectively. In this paper, the simulated aerosol chemical, physical, and optical properties and their consistency were evaluated using various observation data in East Asia. The simulated mass, size, and deposition of SO42- and NH4+ agreed well with the observations, whereas those of NO3-, sea salt, and dust needed improvement. The simulated surface mass concentration (PM10 and PM2.5) and spherical extinction coefficient agreed well with the observations. The simulated aerosol optical thickness (AOT) and dust extinction coefficient were significantly underestimated. [ABSTRACT FROM AUTHOR]

When assessing the short-term effect of air pollution on health outcomes, it is common practice to consider one pollutant at a time, due to their high correlation. Multi pollutant methods have been recently proposed, mainly consisting of collapsing the different pollutants into air quality indexes or clustering the pollutants and then evaluating the effect of each cluster on the health outcome. A major drawback of such approaches is that it is not possible to evaluate the health impact of each pollutant. In this paper we propose the use of the Bayesian hierarchical framework to deal with multi pollutant concentrations in a two-component model: a pollutant model is specified to estimate the ‘true’ concentration values for each pollutant and then such concentration is linked to the health outcomes in a time-series perspective. Through a simulation study we evaluate the model performance and we apply the modelling framework to investigate the effect of six pollutants on cardiovascular mortality in Greater London in 2011-2012. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting model with Chemistry (WRF/Chem) simulation with the 2005 Carbon Bond (CB05) gas-phase mechanism coupled to the Modal for Aerosol Dynamics for Europe (MADE) and the volatility basis set approach for secondary organic aerosol (SOA) are conducted over a domain in North America for 2006 and 2010 as part of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 project. Following the Part 1 paper that focuses on the evaluation of the 2006 simulations, this Part 2 paper focuses on a comparison of model performance in 2006 and 2010 as well as analysis of the responses of air quality and meteorology-chemistry interactions to changes in emissions and meteorology from 2006 to 2010. In general, emissions for gaseous and aerosol species decrease from 2006 to 2010, leading to a reduction in gaseous and aerosol concentrations and associated changes in radiation and cloud variables due to various feedback mechanisms. WRF/Chem is able to reproduce most observations and the observed variation trends from 2006 to 2010, despite its slightly worse performance than WRF that is likely due to inaccurate chemistry feedbacks resulting from less accurate emissions and chemical boundary conditions (BCONs) in 2010. Compared to 2006, the performance for most meteorological variables in 2010 gives lower normalized mean biases but higher normalized mean errors and lower correlation coefficients. The model also shows poorer performance for most chemical variables in 2010. This could be attributed to underestimations in emissions of some species, such as primary organic aerosol in some areas of the US in 2010, and inaccurate chemical BCONs and meteorological predictions. The inclusion of chemical feedbacks in WRF/Chem reduces biases in meteorological predictions in 2010; however, it increases errors and weakens correlations comparing to WRF simulations. Sensitivity simulations show that the net changes in meteorological variables from 2006 to 2010 are mostly influenced by changes in meteorology and those of ozone and fine particulate matter are influenced to a large extent by emissions and/or chemical BCONs and to a lesser extent by changes in meteorology. Using a different set of emissions and/or chemical BCONs helps improve the performance of individual variables, although it does not improve the degree of agreement with observed interannual trends. These results indicate a need to further improve the accuracy and consistency of emissions and chemical BCONs, the representations of SOA and chemistry-meteorology feedbacks in the online-coupled models. [ABSTRACT FROM AUTHOR]

In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined. [ABSTRACT FROM AUTHOR]

This paper presents the Lagrangian particle model LAPMOD for modeling time-variable emissions in atmosphere of inert and radioactive gases and aerosols. LAPMOD is fully interfaced with the meteorological model CALMET (Scire et al., 1999a), part of the US-EPA recommended CALPUFF modeling system (EPA, 2017), and can also use the meteorological input files produced with the AERMET meteorological processor of US-EPA recommended model AERMOD (EPA, 2004). The paper outlines the theory on which LAPMOD is based and provides the results of the evaluation of LAPMOD against the Kincaid SF 6 and SO 2 classical field studies and tracer experiments. The performance of LAPMOD is successfully evaluated with the Model Evaluation Kit (Olesen, 2005) and compared with that of other air quality models. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years'' (2000--06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality--related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047--53) and present (2000--06) simulation results driven by PCM data and is presented in a separate paper. [ABSTRACT FROM AUTHOR]

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Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. [ABSTRACT FROM AUTHOR]

The spatial distribution of trace gases exhibit large spatial heterogeneity over the Indian region with an elevated pollution loading over densely populated Gangetic Plains (IGP). The contending role and importance of anthropogenic emissions and meteorology in deciding the trace gases level and distribution over Indian region, however, is poorly investigated. In this paper, we use an online regional chemistry transport model (WRF/Chem) to simulate the spatial distribution of trace gases over Indian region during one representative month of only three meteorological seasons namely winter, spring/summer and monsoon. The base simulation, using anthropogenic emissions from SEACRS inventory, is used to simulate the general meteorological conditions and the realistic spatial distribution of trace gases. A sensitivity simulation is conducted after removing the spatial heterogeneity in the anthropogenic emissions, i.e., with spatially uniform emissions to decouple the role of anthropogenic emissions and meteorology and their role in controlling the distribution of trace gases over India. The concentration levels of Ozone, CO, SO and NO were found to be lower over IGP when the emissions are uniform over India. A comparison of the base run with the sensitivity run highlights that meteorology plays a dominant role in controlling the spatial distribution of relatively longer-lived species like CO and secondary species like Ozone while short-lived species like NO and SO are predominantly controlled by the spatial variability in anthropogenic emissions over the Indian region. [ABSTRACT FROM AUTHOR]

Air pollution is caused by variety of sources such as industries, vehicles, cremation, bakeries, and open burning. These sources have variation in emission with different time scales. Industry and bakeries have variation in emission with day or week, rest of the sources like vehicles and domestic sector have variation with time in a day. In fact, vehicles have a large variation in emission with time period of the day. The average concentration of 24 h is much less than hourly concentration of peak time when there is heavy vehicular emissions. The hourly concentration of off-peak time or lean time is very low due to low emission for that period. The air quality standards of India are prescribed for 24-h average concentration with which the predicted average concentration from models is compared. However, the peak time concentration may be much higher than the standard. In the peak time, outdoor concentration is more and since a large proportion of the population is out the exposure is also very high and can cause severe health effect. In this paper, vehicular pollution modeling has been carried out using AERMOD with simulated meteorology by Weather Research and Forecasting model. NO and PM concentrations were 3.6 and 1.45 times higher in peak time than off-peak and evening peak, respectively. Lean time has higher concentration for both NO and PM than off-peak and evening peak. It shows the misleading concept of comparing average predicted concentration of 24 h with standards for vehicles. [ABSTRACT FROM AUTHOR]

This study investigates the fine particulate matter (PM2.5) variability in the Klang Valley urban-industrial environment. In total, 94 daily PM2.5 samples were collected during a 1-year campaign from August 2011 to July 2012. This is the first paper on PM2:5 mass, chemical composition and sources in the tropical environment of Southeast Asia, covering all four seasons (distinguished by the wind flow patterns) including haze events. The samples were analysed for various inorganic components and black carbon (BC). The chemical compositions were statistically analysed and the temporal aerosol pattern (seasonal) was characterised using descriptive analysis, correlation matrices, enrichment factor (EF), stoichiometric analysis and chemical mass closure (CMC). For source apportionment purposes, a combination of positive matrix factorisation (PMF) and multi-linear regression (MLR) was employed. Further, meteorological-gaseous parameters were incorporated into each analysis for improved assessment. In addition, secondary data of total suspended particulate (TSP) and coarse particulate matter (PM10) sampled at the same location and time with this study (collected by Malaysian Meteorological Department) were used for PM ratio assessment. The results showed that PM2:5 mass averaged at 28±18 μgm-3, 2.8-fold higher than the World Health Organisation (WHO) annual guideline. On a daily basis, the PM2.5 mass ranged between 6 and 118 μg m-3 with the daily WHO guideline exceeded 43% of the time. The north-east (NE) monsoon was the only season with less than 50% sample exceedance of the daily WHO guideline. On an annual scale, PM2.5 mass correlated positively with temperature (T ) and wind speed (WS) but negatively with relative humidity (RH). With the exception of NOx , the gases analysed (CO, NO2, NO and SO2) were found to significantly influence the PM2.5 mass. Seasonal variability unexpectedly showed that rainfall, WS and wind direction (WD) did not significantly correlate with PM2.5 mass. Further analysis on the PM2.5 =PM10, PM2.5 = TSP and PM10 = TSP ratios reveal that meteorological parameters only greatly influenced the coarse particles (particles with an aerodynamic diameter of greater than 2.5 μm) and less so the fine particles at the site. Chemical composition showed that both primary and secondary pollutants of PM2:5 are equally important, albeit with seasonal variability. The CMC components identified were in the decreasing order of (mass contribution) BC> secondary inorganic aerosols (SIA) > dust > trace elements > sea salt >K+. The EF analysis distinguished two groups of trace elements: those with anthropogenic sources (Pb, Se, Zn, Cd, As, Bi, Ba, Cu, Rb, V and Ni) and those with a crustal source (Sr, Mn, Co and Li). The five identified factors resulting from PMF 5.0 were (1) combustion of engine oil, (2) mineral dust, (3) mixed SIA and biomass burning, (4) mixed traffic and industrial and (5) sea salt. Each of these sources had an annual mean tribution of 17, 14, 42, 10 and 17% respectively. The dominance of each identified source largely varied with changing season and a few factors were in agreement with the CMC, EF and stoichiometric analysis, accordingly. In relation to meteorological-gaseous parameters, PM2.5 sources were influenced by different parameters during different seasons. In addition, two air pollution episodes (HAZE) revealed the influence of local and/or regional sources. Overall, our study clearly suggests that the chemical constituents and sources of PM2.5 were greatly influenced and characterised by meteorological and gaseous parameters which vary greatly with season. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) and its fraction PM10, belong to the major air pollutants in urban environment, because of their effects on air quality and human health. It is important to study in greater details the impact of different PM10 sources on the mass concentration when exceeding the air quality limit and its target values, observed in Sofia. In order to investigate the PM10 variations in connection with the elemental composition and the influence of the meteorological conditions, winter and summer campaigns with standardized low-volume PM10 sampler were organized at National Institute of Meteorology and Hydrology (NIMH) Sofia, in 2012. In the present paper the results for PM10 mass concentration and its elemental composition are compared and discussed. In general PM10 concentrations are higher in February (17.7-160.1 µg.m-3) than those measured in July (15.5-42.7 µg.m-3), because of the different meteorological conditions. The ED-XRF technique is applied to determine more than 20 elements (P, S, CI, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn Br, R Sr, Y, Zr, Cd, Sn, Sb, I, Ba, Pb) in the filter samples. The concentrations of some of the elements are under the detection limits of EDXDR, in particular for the PM10 summer filter samples. The elemental concentrations are lower in July than in February with an exception for the crustal elements Ca and Fe. [ABSTRACT FROM AUTHOR]

Abstract: Traditional Eulerian air quality models are unable to accurately simulate sub-grid scale processes, such as the near-source transport and chemistry of point source plumes, because they assume instantaneous mixing of the emitted pollutants within the grid cell containing the release, and neglect the turbulent segregation effects that limit the near-source mixing of emitted pollutants with the background atmosphere (e.g., Kramm and Meixner, 2000). Observations by Dlugi et al. (2010) show that the segregation of chemically reactive species can slow effective second-order reaction rates by as much as 15%, due to inhomogeneous mixing of the reactants. This limitation of traditional grid models applies to both “off-line” models, in which externally derived meteorology is used to drive the chemistry model, and newer “on-line” models, such as the Weather Research and Forecasting model with Chemistry (WRF/Chem), that simulate the emissions, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. While a number of approaches have been used in the past to address this limitation, the approach that has been most effectively used in operational models is the plume-in-grid (PinG) approach, in which a reactive plume model is embedded within the grid model to resolve sub-grid scale plumes. This paper describes the implementation of such a PinG treatment in WRF/Chem, based on a similar extension to the U.S. EPA Community Multi-scale Air Quality (CMAQ) model. The treatment, referred to as Advanced Plume Treatment, has been tested in CMAQ over more than a decade and has been used successfully in both episodic and long-term applications for assessing point source contributions to ozone and particulate matter. This paper presents the application of the PinG version of WRF/Chem for a three-day episode in July 2001, including a model performance evaluation and comparison of model results with and without PinG treatment. The results from the model application show that overall model performance is only slightly affected when the PinG treatment is used, although there are some generally small improvements, with the PinG treatment showing a 5% lower bias in predicting ozone concentrations, and 3% lower bias in sulfate predictions. However, the predicted spatial patterns of ozone and PM2.5 concentrations from the two simulations show both large decreases of up to 40 ppb ozone and 14 μg m−3 PM2.5, and increases of up to 80 ppb ozone and 33 μg m−3 PM2.5 as a result of using the PinG treatment. These differences are attributed to both direct effects of the PinG treatment (i.e., differences in dispersion, transport and chemistry of point source emissions) and indirect effects (i.e., impacts of air quality changes on meteorology). [Copyright &y& Elsevier]

This paper presents the main experimental results of surface ozone concentrations measured at a rural area in Northern Spain from February 2000 to December 2005. Daily and seasonal variation of ozone has been analysed. The peak concentration levels are obtained in the afternoon, mean value around 88 µg m−3, with extreme average values of 59 µg m−3 in January and 113 µg m−3 in July. Small differences are found in the mean and median of the ozone levels from April to August, when spring and summer maxima are observed. Despite the great inter-annual ozone variability obtained, most air quality indicators showed a positive trend. Further analysis of the monthly mean ozone concentrations of the main percentiles have also been performed using a harmonic model. The upward trend was 6.2 ± 1.7 µg m−3 for the 98th percentile. To interpret the main features of the annual cycle and describe the ozone peaks, the influence of meteorological factors is studied. In summer, ozone production is governed by local processes, air temperature being the major controlling factor. However, the origin of the spring maximum is not so well identified. [ABSTRACT FROM AUTHOR]

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena. [ABSTRACT FROM AUTHOR]

The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project "Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure" (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue. [ABSTRACT FROM AUTHOR]

In this study, the relationship between inhalable particulate (PM10), fine particulate (PM2.5), coarse particles (PM2.5 – 10) and meteorological parameters such as temperature, relative humidity, solar radiation, wind speed were statistically analyzed and modelled for urban area of Kolkata during winter months of 2003–2004. Ambient air quality was monitored with a sampling frequency of twenty-four hours at three monitoring sites located near traffic intersections and in an industrial area. The monitoring sites were located 3–5 m above ground near highly trafficked and congested areas. The 24 h average PM10 and PM2.5 samples were collected using Thermo-Andersen high volume samplers and exposed filter papers were extracted and analysed for benzene soluble organic fraction. The ratios between PM2.5 and PM10 were found to be in the range of 0.6 to 0.92 and the highest ratio was found in the most polluted urban site. Statistical analysis has shown a strong positive correlation between PM10 and PM2.5 and inverse correlation was observed between particulate matter (PM10 and PM2.5) and wind speed. Statistical analysis of air quality data shows that PM10 and PM2.5 are showing poor correlation with temperature, relative humidity and solar radiation. Regression equations for PM10 and PM2.5 and meteorological parameters were developed. The organic fraction of particulate matter soluble in benzene is an indication of poly aromatic hydrocarbon (PAH) concentration present in particulate matter. The relationship between the benzene soluble organic fraction (BSOF) of inhalable particulate (PM10) and fine particulate (PM2.5) were analysed for urban area of Kolkata. Significant positive correlation was observed between benzene soluble organic fraction of PM10 (BSM10) and benzene soluble organic fraction of PM2.5 (BSM2.5). Regression equations for BSM10 and BSM2.5 were developed. [ABSTRACT FROM AUTHOR]

Physical parameterisations of turbulent transfer processes in the atmospheric boundary layer, such as the stability parameterisations developed by Joost Businger, and recent advances in computing capabilities, have been important factors leading to the emergence of operational, numerical air quality forecasting systems. The present paper investigates the performance of the Australian Air Quality Forecasting System (AAQFS) in forecasting the peak 1 h ozone for the current or next day. These 24/36 h forecasts are generated for the Sydney and Melbourne regions and issued twice daily. Quantitative evidence is presented of the potential for the AAQFS to provide accurate numerical air quality forecasts. A second goal is to provide an initial benchmark for investigating the limits of predictability for air quality in the Sydney and Melbourne regions by looking at the dependence of the forecasts on the domain spatial scale (while maintaining the same model grid resolution), the starting time and length of the forecast (0000 UTC starts are 36-h forecasts and 1200 UTC starts are 24-h forecasts), and the sophistication of the photochemical mechanism (simple chemistry, Generic Reaction Set (GRS) and complex chemistry, Carbon Bond IV (CBIV)). The probability of detection by the forecast model is much better than persistence, showing considerable skill. The normalised bias, in general, decreases going from regional scale to sub-regional scale and becomes negative at the station scale. In Melbourne the gross error increases as the domain spatial scale decreases, but in Sydney there is a dip in the error at the sub-regional scale due to a sampling artifact. Better results are obtained at the smaller domain scales for 1200 UTC forecasts in Sydney. These are attributed to the shorter forecast period and secondarily to greater model spin-up effects at 0000 UTC. In Melbourne the results are ambiguous. Similar conclusions are derived from scatter plots of forecasts versus observations. Dividing the scatter plots into four sections by plotting vertical and horizontal lines (at 60 ppb) forms contingency tables for categorical forecasting. These plots show the increase in missed forecasts due to underprediction and the decrease in the number of extreme events detected as the spatial scale decreases. A comparison of the highly condensed GRS photochemical mechanism with the comprehensive CBIV mechanism indicates that, in general, GRS performs well for predicting ozone in urban situations provided that the background concentrations are appropriately specified. The potential to improve the forecasts at the smaller spatial scales, particularly for extreme events at high ozone concentrations, may require moving to a more complex mechanism as computer resources become available. [ABSTRACT FROM AUTHOR]

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In 2019, Malaysia faced a deterioration of air quality due to transboundary haze, which brought negative implications, especially for public health. In light of the above scenario, continuous particulate matter (PM10, PM2.5 and PM1) and meteorological parameters amid the haze period were taken to unravel the influence of haze on particulate matter variations and to investigate the association between particulate matter concentrations with meteorological parameters and fire hotspots in Kota Kinabalu, where it is rarely studied. Particulate matter and the meteorological parameters were monitored during the haze season, continuously from 21 August-30 September 2019, using AirMate, a ground-based air monitoring equipment. Air mass backward trajectories were simulated using the HYSPLIT Model, and fire hotspot data was obtained from the Greenpeace Global Fire Dashboard. The results showed increasing particulate matter concentrations during the haze period, with PM2.5 exceeding the New Ambient Air Quality Standards (2020) on multiple days. For meteorological parameters, all parameters showed a significant weak positive relationship with respective particulate matter. However, the correlation between particulate matter and fire hotspots in Indonesia showed a moderate positive relationship. The backward trajectories simulated indicated the influence of southwesterly winds in transporting the pollutants from fire hotspots in the Indonesia region. Thus, we provide beneficial information about the impacted area during the 2019 transboundary haze episode, where the interactions between the particulate matter variations and the parameters studied were unraveled. [ABSTRACT FROM AUTHOR]

Fumigation under sea-breeze conditions is a major feature of the air pollution meteorology in the coastal industrial region of Kwinana, south of Perth in Western Australia. An intensive field experiment on fumigation was carried out in the region in early 1995 with the objective of using the measured data to develop and test a shoreline fumigation model. Fumigation of plumes from the Kwinana Power Station was studied using an instrumented research aircraft, radiosonde balloons, meteorological stations, a lidar, a mobile surface sampler, and sonic anemometers. The study has yielded a detailed and high quality data set as a result of both the range of observations undertaken and of the regularity of the sea-breeze conditions under scrutiny. The details of the experiment are summarised in this paper and some typical results are presented. [ABSTRACT FROM AUTHOR]

In recent years, main cities in China have been suffering from hazy weather, which is gaining great attention among the public, government managers and researchers in different areas. Many studies have been conducted on the topic of urban air quality to reveal different aspects of the air quality problem in China. This paper focuses on the visualization problem of the big air quality monitoring data of all main cities on a nationwide scale. To achieve the intuitive visualization of this dataset, this study develops two novel visualization tools for multi-granularity time series visualization (timezoom.js) and a dynamic symbol declutter map mashup layer for thematic mapping (symadpative.js). With the two invented tools, we develops an interactive web map visualization application of urban air quality data of all main cities in China. This application shows us significant air pollution findings at the nationwide scale. These results give us clues for further studies on air pollutant characteristics, forecasting and control in China. As the tools are invented for general visualization purposes of geo-referenced time series data, they can be applied to other environmental monitoring data (temperature, precipitation, etc.) through some configurations. [ABSTRACT FROM AUTHOR]