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Levoglucosan is used as a tracer of biomass burning; however, its determination often requires expensive analytical techniques. Therefore, this study describes the development of an inexpensive and useful method using paper-based analytical devices (PADs) to determine levoglucosan based on colorimetric measurements. The color intensities were correlated with the analytical concentrations and presented linear behavior in the range from 0 to 64.8 μg mL−1. The achieved limits of detection and quantification were 2 and 6 μg mL−1, respectively. The conversion rate of levoglucosan into glucose was 81 ± 8% and the average recovery was 105 ± 9%. Moreover, the method presented selectivity for levoglucosan, showing variation in colorimetric signal intensity lower than 8% in the presence of other saccharides (xylose, glucose, galactose, maltose, mannose, arabinose, and fructose). The accuracy of the method was confirmed by comparison with gas chromatography-mass spectrometry. The proposed method was explored to determine levoglucosan in samples of atmospheric particulate matter collected in Goiania city (Brazil) and the values ranged from 0.08 to 1.10 μg m−3, showing the high impact of the biomass burning to the particulate matter in the region's atmosphere. The method was also used to detect levoglucosan in rainwater samples. Therefore, the use of PADs can simplify fieldwork involving the determination of levoglucosan in atmospheric particulate matter. Image 1 • Paper-based analytical devices were used to determine levoglucosan. • The method is inexpensive, useful and presented selectivity for levoglucosan. • The highest levoglucosan concentration was determined in the dry season. • The particulate matter chemical composition was highly affected by biomass burning. [ABSTRACT FROM AUTHOR]

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]

Abstract In recent years, the forecasting of particles with a diameter of 2.5 μm or less (PM 2.5) has been a popular research topic, and involves multiple sources of pollution, making it difficult to determine all of the contributing meteorological and environmental factors. When only the PM 2.5 concentration time series is considered without other exogenous information, accurate forecasting is important and should be efficient. To address this problem, this paper proposes a hybrid algorithm consisting of multiple models to improve prediction accuracy. The innovation of the proposed hybrid algorithm is to decompose the original single one-dimensional (1D) PM 2.5 data into multi-dimensional information which effectively mines information hidden in the 1D data. Then, it uses traditional prediction methods to forecast each sequence and to reconstruct its forecasting results to obtain the final forecasting results. Three hybrid models, Wavelet-ANN, Wavelet-ARIMA and Wavelet-SVM, are developed to forecast the 2016 PM 2.5 trends in 5 Cities in China. The results showed that: (1) Hybrid models (Wavelet-ANN, Wavelet-ARIMA and Wavelet-SVM) can forecast short-term PM 2.5 concentrations in China. Compared with the traditional Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) models, hybrid models can significantly improve prediction accuracy. (2) The Wavelet-ARIMA model has higher accuracy with respect to predicting PM 2.5 concentrations. In particular, it can more accurately capture the mutational points of PM 2.5 concentrations, which can provide effective information support for generating warnings about atmospheric pollution. The hybrid algorithm proposed in this paper can be effectively applied to the short-term forecasting of PM 2.5 concentrations and can significantly improve the accuracy of prediction. Graphical abstract Image 1 Highlights • Wavelet decomposition can mine as much information hidden in the data as possible. • We proposed a hybrid algorithm consisting of multiple models to improve prediction accuracy. • The hybrid algorithm is a good alternative for forecasting of PM 2.5 when well designed. [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]

Machine learning methods are increasingly being used in the field of air quality research to investigate the relationship between air pollutant levels, emissions, and meteorological changes over time. This research is used for both scientific investigation, and policy assessment and development. However, there is a lack of studies that have compared the performance of different machine learning methods. To address this gap, this paper employed various machine learning techniques, including decision tree, random forest (RF), support vector machine (SVM), support vector regression (SVR), k-nearest neighbor, neural network, and Gaussian process regression, to predict daily average PM 2.5 levels and the number of days with PM 2.5 exceedance in the South Coast Air Basin of California from 2000 to 2019. The models were trained using meteorological factors, estimated emissions, and large-scale climate indices as inputs. The SVR model demonstrated the highest predictive accuracy for PM 2.5 levels and the SVM model gave the most accurate results for predicting the number of days with PM 2.5 exceedances. Conversely, the decision tree model performed the least accurately. The results also showed that emissions have a greater impact on PM 2.5 levels over time compared to meteorological factors, though meteorology is responsible for daily variability. The most important meteorological factors were identified as surface relative humidity and relative humidity at 850 mbars, which are related to partitioning, cloud cover and wet deposition. We conducted sensitivity tests on the model's response to emissions and meteorological factors. The predicted PM 2.5 from RF and SVR showed large correlations with emissions at the early period (2000–2010). However, the changes were minimal in more recent years (2011–2019), implying that there are biases in machine learning models, in which the models consistently predict the minimum PM 2.5 levels at a baseline. [Display omitted] • SVR is more accurate on daily PM 2.5 , particularly PM 2.5 exceedances predictions. • Surface RH was the most important meteorological factor for PM 2.5 prediction. • The impact of emissions on PM 2.5 was significant before 2010 but reduced thereafter. • ML models predict past better than future; all the ML models are limited at extremes. [ABSTRACT FROM AUTHOR]

The PM 2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM 2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM 2.5 . In this paper, we attempted to realize the relevance analysis and short-term prediction of PM 2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM 2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO 2 , SO 2 , PM 10 ) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO 2 , PM 10 , and the daily number of microblog entries with key words ‘Beijing; Air pollution’ show high mathematical correlation with PM 2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM 2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM 2.5 and it also helps to broaden the application of big data and the multi-source data mining methods. [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]

Epidemiologic studies have consistently observed associations between fine particulate matter (PM 2.5) exposure and premature mortality. These studies use air quality concentration information from a combination of sources to estimate pollutant exposures and then assess how mortality varies as a result of differing exposures. Health impact assessments then typically use a single log-linear hazard ratio (HR) per health outcome to estimate counts of avoided human health effects resulting from air quality improvements. This paper estimates the total PM 2.5 -attributable premature mortality burden using a variety of methods for estimating exposures and quantifying PM 2.5 -attributable deaths in 2011 and 2028. We use: 1) several exposure models that apply a wide range of methods, and 2) a variety of HRs from the epidemiologic literature that relate long-term PM 2.5 exposures to mortality among the U.S. population. We then further evaluate the variability of aggregated national premature mortality estimates to stratification by race and/or ethnicity or exposure level (e.g., below the current annual PM 2.5 National Ambient Air Quality Standards). We find that unstratified annual adult mortality burden incidence estimates vary more (e.g., ∼3-fold) by HR than by exposure model (e.g., <10%). In addition, future mortality burden estimates stratified by race/ethnicity are larger than the unstratified estimates of the entire population, and studies that stratify PM 2.5 -attributable mortality HRs by an exposure concentration threshold led to substantially higher estimates. These results are intended to provide transparency regarding the sensitivity of mortality estimates to upstream input choices. • PM 2.5 -attributable mortality burden estimates vary, but are becoming more precise. • Hazard ratios contribute more mortality burden variability than exposure models. • Race/ethnicity-stratifications increase 2028 mortality burden estimates. • Exposure-stratifications increase 2011 and 2028 mortality burden estimates. [ABSTRACT FROM AUTHOR]

Urban traffic arteries are considered as hot-spots of pollution of particle number concentration (PNC), but the knowledge of spatial variability and exposure levels of PNC with different particle sizes emitted by vehicles is yet less reported. In this paper, PNC in the size range from 6 to 10,000 nm were measured using the Electrical Low-Pressure Impactor Plus (ELPI+) at four sites near high-density traffic in Xi'an, including one traffic, one roadside, and two urban background. The results showed that PNC was highest at the distance closest to the traffic sources, and decreased exponentially with distance. Vehicle exhaust was the primary source of ambient ultrafine particle (UFP), contributing 60.03% and 54.48% of the number concentration of UFP in the clean and haze weathers, respectively. The average PNCs measured at the traffic site were 2.24 and 3.17 times greater than those at the roadside and urban background sites, respectively. Coarse mode particle concentrations exhibited a higher level at the roadside site due to the secondary dust generated by vehicles. An interesting phenomenon that an inverted U-shaped relationship between measured UFP levels and traffic flows was found when the flow was higher than 6000 veh h−1. Strong spatial inhomogeneity for UFP along the urban road, whereas particles larger than 100 nm in diameter tend to similar concentration distributions. The mean respiratory deposition dose (RDD) at four locations was (7.14 ± 1.12) × 105 particles h−1, with the highest (14.61 ± 2.29) × 1010 particles h−1 at the traffic site. Retired people had a higher exposure of UFP compared to children and adults. This improved understanding of the impact of vehicle emissions on the distribution of PNC near-road. [Display omitted] • PNC with sizes of 6–10,000 nm was sampled at four sites near a traffic arterial. • Spatial variability of PNC and their exposure levels were analyzed. • An inverted U-shaped relationship between PNCs and traffic volumes. • Vehicles contribute 60% of UFP number concentration in the traffic environment. • High PNC exposure in retirees should be a concern. [ABSTRACT FROM AUTHOR]

This study aims to investigate distribution and sources of 16 polycyclic aromatic hydrocarbons (PAHs) bound to fine particulate matter (PM 2.5 ) captured in a semi-urban area in Malaysia during different seasons, and to assess their health risks. PM 2.5 samples were collected using a high volume air sampler on quartz filter paper at a flow rate of 1 m 3 min −1 for 24 h. PAHs on the filter paper were extracted with dichloromethane (DCM) using an ultrasonic centrifuge solid-phase extraction method and measured by gas chromatography–mass spectroscopy. The results showed that the range of PAHs concentrations in the study period was between 0.21 and 12.08 ng m −3 . The concentrations of PAHs were higher during the south-west monsoon (0.21–12.08 ng m −3 ) compared to the north-east monsoon (0.68–3.80 ng m −3 ). The high molecular weight (HMW) PAHs (≥5 ring) are significantly prominent (>70%) compared to the low molecular weight (LMW) PAHs (≤4 ring) in PM 2.5 . The Spearman correlation indicates that the LMW and HMW PAHs correlate strongly among themselves. The diagnostic ratios (DRs) of I[c]P/I[c]P + BgP and B[a]P/B[g]P suggest that the HMW PAHs originated from fuel combustion sources. The source apportionment analysis of PAHs was resolved using DRs-positive matrix factorization (PMF)-multiple linear regression (MLR). The main sources identified were (a) gasoline combustion (65%), (b) diesel and heavy oil combustion (19%) and (c) natural gas and coal burning (15%). The health risk evaluation, by means of the lifetime lung cancer risk (LLCR), showed no potential carcinogenic risk from the airborne BaP eq (which represents total PAHs at the present study area in Malaysia). The seasonal LLCR showed that the carcinogenic risk of total PAHs were two fold higher during south-westerly monsoon compared to north-easterly monsoon. [ABSTRACT FROM AUTHOR]

This paper presents a detailed emission inventory for NO x , particle mass (PM) and particle numbers (PN) for aircraft main engines, APU's and handling equipment at Copenhagen Airport (CPH) based on time specific activity data and representative emission factors for the airport. The inventory has a high spatial resolution of 5 m × 5 m in order to be suited for further air quality dispersion calculations. Results are shown for the entire airport and for a section of the airport apron area (“inner apron”) in focus. The methodology presented in this paper can be used to quantify the emissions from aircraft main engines, APU and handling equipment in other airports. For the entire airport, aircraft main engines is the largest source of fuel consumption (93%), NO x , (87%), PM (61%) and PN (95%). The calculated fuel consumption [ NO x , PM, PN ] shares for APU's and handling equipment are 5% [ 4%, 8%, 5% ] and 2% [ 9%, 31%, 0% ], respectively. At the inner apron area for handling equipment the share of fuel consumption [NO x , PM, PN] are 24% [ 63%, 75%, 2% ], whereas APU and main engines shares are 43% [ 25%, 19%, 54% ], and 33% [ 11%, 6%, 43% ], respectively. The inner apron NO x and PM emission levels are high for handling equipment due to high emission factors for the diesel fuelled handling equipment and small for aircraft main engines due to small idle-power emission factors. Handling equipment is however a small PN source due to the low number based emission factors. Jet fuel sulphur-PM sensitivity calculations made in this study with the ICAO FOA3.0 method suggest that more than half of the PM emissions from aircraft main engines at CPH originate from the sulphur content of the fuel used at the airport. Aircraft main engine PN emissions are very sensitive to the underlying assumptions. Replacing this study's literature based average emission factors with “high” and “low” emission factors from the literature, the aircraft main engine PN emissions were estimated to change with a factor of 14. [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: This paper presents the application of an ensemble forecasting approach to the Prev’Air operational platform. This platform aims at forecasting maps, on a daily basis, for ozone, nitrogen dioxide and particulate matter. It relies on several air quality models which differ by their physical parameterizations, their input data and numerical strategies, so that one model may perform better with respect to observations for a given pollutant, at a given time and location. We apply sequential aggregation methods to this ensemble of models, which has already proved good potential in previous research papers. Compared to these studies, the novelties of this paper are the variety of models, the real operational context, which requires robustness assessment, and the application to several pollutants. In this paper, we first introduce the ensemble forecasting methods and the operational platform Prev’Air along with its models. Then, the sequential aggregation performance and robustness are assessed using two different data sets. The results with the discounted ridge regression method show that the errors of the forecasts are respectively reduced by at least 29%, 35% and 19% for hourly, daily and peak O3 concentrations, by 19%, 26% and 20% for hourly, daily and peak NO2 concentrations, and finally by 17%, 19% and 11% for hourly, daily and peak PM10 concentrations. At last, we give a first insight of the ensemble ability to forecast threshold exceedances. [Copyright &y& Elsevier]

Abstract: Estimates of future emissions are necessary for understanding the future health of the atmosphere, designing national and international strategies for air quality control, and evaluating mitigation policies. Emission inventories are uncertain and future projections even more so, thus it is important to quantify the uncertainty inherent in emission projections. This paper is the second in a series that seeks to establish a more mechanistic understanding of future air pollutant emissions based on changes in technology. The first paper in this series (Yan et al., 2011) described a model that projects emissions based on dynamic changes of vehicle fleet, Speciated Pollutant Emission Wizard-Trend, or SPEW-Trend. In this paper, we explore the underlying uncertainties of global and regional exhaust PM emission projections from on-road vehicles in the coming decades using sensitivity analysis and Monte Carlo simulation. This work examines the emission sensitivities due to uncertainties in retirement rate, timing of emission standards, transition rate of high-emitting vehicles called “superemitters”, and emission factor degradation rate. It is concluded that global emissions are most sensitive to parameters in the retirement rate function. Monte Carlo simulations show that emission uncertainty caused by lack of knowledge about technology composition is comparable to the uncertainty demonstrated by alternative economic scenarios, especially during the period 2010–2030. [Copyright &y& Elsevier]

Abstract: Particulate matter (PM10) associated with polycyclic aromatic hydrocarbons (PAHs) in ambient air were determined at two sites within the Klang Valley, Kuala Lumpur (urban, KL) and Petaling Jaya (industrial, PJ), and one site outside the Klang Valley, Bangi (semi-urban, BG). This study aimed to determine the concentration and distribution of PAHs in PM10 and the source of origin through principal component analysis (PCA) and diagnostic ratio analysis. This study also assessed the health risk from exposure to airborne BaPeq. PM10 samples were collected on glass fiber filter paper using a High Volume Sampler (HVS) for 24 h between September 2010 and April 2011. The filter papers with PM10 were extracted using dichloromethane-methanol (3:1), and analysis of 16 USEPA priority PAHs was determined using gas chromatography with mass spectra (GC–MS). Health risk assessment was estimated using toxic equivalency factors (TEFs) and incremental lifetime cancer risk (ILCR) which quantitatively estimate the exposure risk for age specific group. The results showed that the total PAHs concentrations throughout seasonal monsoons for KL, PJ and BG ranged from 1.33 ng m−3 to 2.97 ng m−3, 2.24 ng m−3 to 4.84 ng m−3 and 1.64 ng m−3 to 3.45 ng m−3 respectively. More than 80% of total PAHs consisted of 5-ring and 6-ring PAHs such as benzo[a]pyrene (BaP), indeno[1,2,3-cd]pyrene (IcP), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF) and benzo[g,h,i]perylene (BgP). The presence of benzo[g,h,i]perylene (BgP) with high concentration at all locations suggested a source indicator for traffic emission. PCA and diagnostic ratio analysis also suggested substantial contributions from traffic emission with minimal influence from coal combustion and natural gas emissions. The use of total BaPeq concentration provide a better estimation of carcinogenicity activities, where they contributed to more than 50% of the potential health risk. Health risk assessment showed that the estimated incremental lifetime cancer risk (ILCR) from exposure to airborne BaPeq is negligible at all sampling sites for all age specific group. [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: Estimating measurement uncertainty for NO2 and PM10 is a complex issue that is normally addressed by experimentalists specifically for every type of instrument and measurement. On the other hand, an estimate of the maximum expected measurement uncertainty is needed when a numerical model is to be evaluated against observations, as proposed in Thunis et al. (2012, referred to as T2012). In a companion paper (Thunis et al., 2013, referred to as T2013) a simplified formulation of the measurement uncertainty in function of the measured concentration is proposed and applied to the simpler case of Ozone. In this paper the same approach is applied for NO2 and PM10, but using different techniques for the uncertainty estimation. For NO2 the Guide to the expression of Uncertainty in Measurement JCGM (2008, referred to as GUM) approach is used and applied on each urban AirBase (1997) measurement over the year 2009. For PM10, the method of the Guide for the Demonstration of Equivalence (ECWG, 2010) is used on data obtained with two different PM samplers used in parallel either during specific monitoring campaign or as available within the AirBase database. The resulting concentration dependent measurement uncertainties are then used to update the MQO (Model Quality Objective) and MPC (Model Performance Criteria) proposed in T2012. An estimate of the measurement uncertainty for annual means is proposed as well. [Copyright &y& Elsevier]

Abstract: This paper, which focuses on diesel trucks, is the third in a series of three papers published in Atmospheric Environment to understand vehicle emissions in China by conducting on-board emission measurements. Diesel trucks are a significant source of emissions in ambient air, especially for NOx. Recently, China announces an aggressive target to reduce national NOx emissions by 10% from 2010 to 2015 in the “Twelfth Five-Year Plan (2011–2015)” and diesel vehicles are identified as a key target for NOx control. However, the understanding of the real-world emissions of diesel trucks is limited. In this study, we measured HC, CO, NOx, and PM2.5 emissions from 175 diesel trucks of different sizes and technologies in five Chinese cities during 2007 and 2011, and generated emission factors on the basis of the measurements. The results show that the HC, CO, and PM2.5 emission factors have been reduced significantly as the emission standards become more stringent from Euro 0 to Euro IV, but the NOx emission factors change differently. Euro II trucks have 3–6% higher NOx emission levels than Euro I technologies and Euro III trucks fail to show a reduction as regulated by the standards. More stringent NOx requirements (e.g. Euro IV) for diesel vehicles need to be enforced. The comparison with the emission factors used in recent emission inventory studies shows that these inventories may have overestimated or underestimated diesel emissions for the years after 2006. This study emphasizes the importance of conducting local measurement research to improve the accuracy of the estimates of mobile emissions in China. [Copyright &y& Elsevier]

Abstract A custom-built steam-based aerosol collector (SBAC) was developed to improve the detection limits in aerosol chemical analysis. The SBAC was coupled with wet chemical analytical instruments to characterize the chemical composition of ambient particulate matter with high-time resolution. The particulate matter was mixed with a jet of steam and formed large droplets because of the supersaturation condition in the cooling chamber. The enlarged droplets were subsequently collected using an inertial aerosol separator and delivered to selected instruments for chemical analysis. Experiments were subsequently conducted to evaluate our custom-built SBAC under various sampling flow rates. In particular, particle growth and particle collection efficiency experiments were conducted, and the SBAC was compared with a filter-based sampler. Sample solutions were collected, extracted, and analyzed to determine the influence of sampling flow rate, particle size, and aerosol chemical concentration on the SBAC's collection efficiency. The SBAC was found to provide data with a higher time resolution, to exhibit an improved detection limit, and to effectively perform aerosol chemical analysis compared with the filter-based samplers, although the effect of flow rate on SBAC performance is complicated and further experiments on this aspect are warranted. This paper contributes to the literature by providing guidelines for designing a SBAC for aerosol chemical analysis. [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 The size distribution of a particle population has proven to be of great value in source apportionment studies. Previous studies have reported that particle size distributions in ambient urban air vary with location and time, indicating that the ambient particles are affected by known sources. However, the size distributions of individual chemical species emitted from primary sources have received relatively little attention. This study presents a preliminary real-world investigation of the size distributions of particles emissions from coal-fired power plants, residential coal combustion, biomass burning, vehicles, soil dust and steelworks based on a high size-resolution measurement using an Electrical Low Pressure Impactor. The size profiles of individual chemical elements are found to have a high degree of source specificity, which assists source classification. We subsequently employed a chemical mass balance approach termed size-resolved CMB (SR-CMB), which is based on the use of both physical and chemical information on the high size-resolution measurements of particulate matter. By applying SR-CMB on the measured ambient data, source contributions to ambient particles were successfully obtained when copper (Cu) was chosen as the fitting species. The approach described in this paper enhances the capability of the CMB model and provides a useful tool to validate results of traditional receptor models when a high size-resolution measurement is available. This effort also highlights that the size profiles of sources is of potential concern in the situations when the chemically collinear sources exist. Highlights • Particles emissions from six sources were measured by using ELPI+. • Size profiles of Cu have a high degree of source specificity. • A size-resolved CMB was successfully employed for source apportionment of PM. [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: In this paper, we are interested in the statistical forecasting of the daily mean PM10 concentration. Hourly concentrations of PM10 have been measured in the city of Rouen, in Haute-Normandie, France. Located at northwest of Paris, near the south side of Manche sea and heavily industrialised. We consider three monitoring stations reflecting the diversity of situations: an urban background station, a traffic station and an industrial station near the cereal harbour of Rouen. We have focused our attention on data for the months that register higher values, from December to March, on years 2004–2009. The models are obtained from the winter days of the four seasons 2004/2005 to 2007/2008 (training data) and then the forecasting performance is evaluated on the winter days of the season 2008/2009 (test data). We show that it is possible to accurately forecast the daily mean concentration by fitting a function of meteorological predictors and the average concentration measured on the previous day. The values of observed meteorological variables are used for fitting the models and are also considered for the test data. We have compared the forecasts produced by three different methods: persistence, generalized additive nonlinear models and clusterwise linear regression models. This last method gives very impressive results and the end of the paper tries to analyze the reasons of such a good behavior. [Copyright &y& Elsevier]

Abstract: In this paper, we present the experimentally determined emission factors and emission estimates of particulate matter (PM), SO2, NO and NO2 emitted from biomass fuels used as energy in rural area of Indo-Gangetic Plain (IGP), India. Biomass fuel samples were collected at district level from this region. The burning of the collected biomass fuels is performed by using the modified dilution sampler based on studies done by . In this study, the emission factor represents the total period of burning including pyrolysis, flaming and smoldering. The average emission factor of PM from dung cake, fuel-wood and crop residue over Delhi, Uttar Pradesh, Punjab, Haryana, Uttarakhand and Bihar are estimated as 16.26 ± 2.29 g kg−1, 4.34 ± 1.06 g kg−1 and 7.54 ± 4.17 g kg−1 respectively. Similarly, the average emission factor of SO2, NO and NO2 from dung cake, fuel-wood and crop residue over this region are also determined (SO2: 0.28 ± 0.09 g kg−1, 0.26 ± 0.10 g kg−1 and 0.27 ± 0.11 g kg−1, NO: 0.27 ± 0.21 g kg−1, 0.41 ± 0.25 g kg−1 and 0.54 ± 0.50 g kg−1 and NO2: 0.31 ± 0.23 g kg−1, 0.35 ± 0.28 g kg−1 and 0.54 ± 0.47 g kg−1 respectively). The emission of PM, SO2, NO and NO2 from biomass fuels used as energy in rural household over, IGP are also estimated in this paper. The result shows the regional emission inventory from Indian scenario with spatial variability. [Copyright &y& Elsevier]

Abstract: This paper explains the first emission inventory for BC and OC for Danish sources related to fuel combustion for the historical 1990–2008 period and the forecast years 2009–2030. The core basis for the BC/OC inventory is the Danish TSP emission inventories and projections. The relevant BC/OC emission factor information is based primarily on the European models GAINS and COPERT IV. Residential sources are the largest contributor to TSP, BC and OC emissions, representing in 2008 70%, 62 % and 83% of the total, followed by road transport exhaust and non exhaust, other mobile sources and other stationary sources. The total Danish emissions of TSP, BC and OC decrease by 14%, 28% and 12%, respectively, from 1990 to 2030. The emissions for Greenland and the Faroe Islands are less than 1% of the Danish total. For residential wood combustion a small number of detailed BC/OC measurements are available from the literature. These emission data are, however, disregarded in the Danish case, either due to lack of combustion unit representation, or data being related to wood type not accounted for in the Danish fuel data. To improve the inventory in this part, more emission measurements are needed especially for new stoves and boilers, which will become increasingly dominant in the future. For private stoves as such, it is desirable to develop a functional filter technology in order to bring down the particulate emissions in the future just as efficiently as for road transport and larger non road mobile machinery types. The work presented in this paper may serve as an input for policy makers dealing with the environmental impacts from combustion related BC and OC emissions. Further the calculated results can be used for air dispersion modelling purposes following a spatial distribution of the detailed emission inventory data. [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]

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 presents a multi-pollutant sensitivity study of an air quality model over Europe with a focus on aerosols. Following the evaluation presented in the companion paper, the aim here is to study the sensitivity of the model to input data, mathematical parameterizations and numerical approximations. To that end, 30 configurations are derived from a reference configuration of the model by changing one input data set, one parameterization or one numerical approximation at a time. Each of these configurations is compared to the same reference simulation over two time periods of the year 2001, one in summer and one in winter. The sensitivity of the model to the different configurations is evaluated through a statistical comparison between the simulation results and through comparisons to available measurements. The species studied are ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), ammonia (NH3), coarse and fine aerosol particles (PM c and PM2.5), sulfate, nitrate, ammonium, chloride and sodium. For all species, the modeled concentrations are very sensitive to the parameterization used for vertical turbulent diffusion and to the number of vertical levels. For the other configurations considered in this work, the sensitivity of the modeled concentration to configuration choice varies with the species and the period of the year. O3 is impacted by options related to boundary conditions. PM c is sensitive to sea-salt related options, to options influencing deposition and to options related to mass transfer between gas and particulate phases. PM2.5 is sensitive to a larger number of options than PM c : sea-salt, boundary conditions, heterogeneous reactions, aqueous chemistry and gas/particle mass transfer. NO2 is strongly influenced by heterogeneous reactions. Nitrate shows the highest variability of all species studied. As with NO2, nitrate is strongly sensitive to heterogeneous reactions but also to mass transfer, thermodynamic related options, aqueous chemistry and computation of the wet particle diameter. While SO2 is mostly sensitive to aqueous chemistry, sulfate is also sensitive to boundary conditions and, to a lesser extent, to heterogeneous reactions. As with nitrate, ammonium is largely impacted by the different configuration choices, although the sensitivity is slightly lower than for nitrate. NH3 is sensitive to aqueous chemistry, mass transfer and heterogeneous reactions. Chloride and sodium are impacted by sea-salt related options, by options influencing deposition and by options concerning the aqueous-phase module. [Copyright &y& Elsevier]

Abstract: This paper characterizes the emission rates of size fractionated particulate matter, inorganic aerosols, acid gases, ammonia and methane measured over four flocks at a commercial broiler chicken facility. Mean emission rates of each pollutant, along with sampling notes, were reported in this paper, the first in a series of two. Sampling notes were needed because inherent gaps in data may bias the mean emission rates. The mean emission rates of PM10 and PM2.5 were 5.0 and 0.78g day−1 [Animal Unit, AU]−1, respectively, while inorganic aerosols mean emission rates ranged from 0.15 to 0.46g day−1 AU−1 depending on the season. The average total acid gas emission rate was 0.43g day−1 AU−1 with the greatest contribution from nitrous and nitric acids and little contribution from sulfuric acid (as SO2). Ammonia emissions were seasonally dependent, with a mean emission rate of 66.0g day−1 AU−1 in the cooler seasons and 94.5g day−1 AU−1 during the warmer seasons. Methane emissions were relatively consistent with a mean emission rate of 208g day−1 AU−1. The diurnal pattern in each pollutant’s emission rate was relatively consistent after normalizing the hourly emissions according to each daily mean emission rate. Over the duration of a production cycle, all the measured pollutants’ emissions increased proportionally to the total live mass of birds in the house, with the exception of ammonia. Interrelationships between pollutants provide evidence of mutually dependent release mechanisms, which suggests that it may be possible to fill data gaps with minimal data requirements. In the second paper (Roumeliotis, T.S., Dixon, B.J., Van Heyst, B.J. Characterization of gaseous pollutants and particulate matter emission rates from a commercial broiler operation part II: correlated emission rates. Atmospheric Environment, 2010.), regression correlations are developed to estimate daily mean emission rates for data gaps and, using the normalized hourly diurnal patterns from this paper, emission factors were generated for each pollutant. [Copyright &y& Elsevier]

Abstract: Particulate matter (PM) has become a major research issue receiving increasing attention because of its significant negative impact on human health. There are main indicators that next to the morphological characteristics of the particle, also the chemical composition plays an important role in the adverse health effects of PM. In this context, the rather polar organic fraction of PM is expected to play a major role, and advanced analytical techniques are developed to improve the knowledge on the molecular composition of this fraction. One component class that deserves major attention consists of the oxygenated polycyclic aromatic hydrocarbons (PAHs). Those compounds are considered to be among the key compounds in PM toxicity. This paper presents a comprehensive review focusing on the analysis, fate and behavior of oxygenated PAHs in the atmosphere. The first part of the paper briefly introduces (i) the main sources and atmospheric pathways of oxygenated PAHs, (ii) available physical–chemical properties and (iii) their health effects. The second and main part of this paper gives a thorough discussion on the entire analytical sequence necessary to identify and quantify oxygenated PAHs on atmospheric PM. Special attention is given to critical parameters and innovations related to (i) sampling, (ii) sample preparation including both extraction and clean-up, and (iii) separation and detection. Third, the state-of-the-art knowledge about the atmospheric occurrence of oxygenated PAHs is discussed, including an extended overview of reported concentrations presented as a function of sampling season and geographical location. A clear seasonal effect is observed with the median of the oxygenated PAHs concentrations during winter being a factor of 3–4 higher than during summer. However, the oxygenated PAH/parent PAH ratio is about 20 times higher during summer, indicating the importance of photochemical activity in the atmosphere. [Copyright &y& Elsevier]

Abstract: Near-surface wind-tunnel fugitive dust concentration profiles arising from soil surfaces beds were compared to a finite difference numerical dust transport model. Comparisons of the type shown in this study were previously non-existent in the literature due to the lack of experimental wind-tunnel data for near-surface concentrations over a soil bed. However, in a previous study by the authors, near-surface steady-state concentration profiles were measured in order to obtain fugitive dust emission rates, thus allowing the comparison to models shown in this paper. The novel aspects of the current study include: comparison of concentration profiles of dust obtained experimentally in the wind tunnel with those calculated numerically; comparison of the calculated numerical fetch effect on dust emissions with that obtained in the wind tunnel; and comparison of the emission rates calculated numerically with those obtained experimentally in the wind tunnel. Initial comparisons with the model indicate good agreement implying that the physical mechanism of advection–diffusion is reasonably modeled with the choice of equations for the simple “steady-state” process near the surface. Furthermore, the numerical solutions presented in this paper provide a means to systematically explore the relative impact of varied surface boundary conditions upon the emission process and provide a potential link between wind-tunnel simulations and field scale models. [Copyright &y& Elsevier]

Abstract: Vehicular emitted air pollutant concentrations were studied near three types of roadways in Austin, Texas: (1) State Highway 71 (SH-71), a heavily traveled arterial highway dominated by passenger vehicles; (2) Interstate 35 (I-35), a limited access highway north of Austin in Georgetown; and (3) Farm to Market Road 973 (FM-973), a heavily traveled surface roadway dominated by truck traffic. Air pollutants examined include carbon monoxide (CO), oxides of nitrogen (NO x ), and carbonyl species in the gas-phase. In the particle phase, ultrafine particle (UFP) concentrations (diameter < 100 nm), fine particulate matter (PM2.5, diameter < 2.5 μm) mass and carbon content and several particle-bound organics were examined. All roadways had an upwind stationary sampling location, one or two fixed downwind sample locations and a mobile monitoring platform that characterized pollutant concentrations fall-off with increased distance from the roadways. Data reported in this paper focus on UFP while other pollutants and near-roadway chemical processes are examined in a companion paper. Traffic volume, especially heavy-duty traffic, wind speed, and proximity to the road were found to be the most important factors determining UFP concentrations near the roadways. Since wind directions were not consistent during the sampling periods, distances along wind trajectories from the roadway to the sampling points were used to study the decay characteristics of UFPs. Under perpendicular wind conditions, for all studied roadway types, particle number concentrations increased dramatically moving from the upwind side to the downwind side. The elevated particle number concentrations decay exponentially with increasing distances from the roadway with sharp concentration gradients observed within 100–150 m, similar to previously reported studies. A single exponential decay curve was found to fit the data collected from all three roadways very well under perpendicular wind conditions. No consistent pattern was observed for UFPs under parallel wind conditions. However, regardless of wind conditions, particle concentrations returned to background levels within a few hundred meters of the roadway. Within measured UFP size ranges, smaller particles (6–25 nm) decayed faster than larger ones (100–300 nm). Similar decay rates were observed among UFP number, surface, and volume. [Copyright &y& Elsevier]

The impact of uncontrolled urban growth (‘sprawl’) on air pollution and associated population exposure is investigated. This is done for the Ruhr area in Germany, by means of a coupled modelling system dealing with land use changes, traffic, meteorology, and atmospheric dispersion and chemistry. In a companion paper [De Ridder, K., Lefebre F., Adriaensen S., Arnold U., Beckroege W., Bronner C., Damsgaard O., Dostal I., Dufek J., Hirsch J., Int Panis L., Kotek Z., Ramadier T., Thierry A., Vermoote S., Wania A., Weber C., 2008. Simulating the impact of urban sprawl on air quality and population exposure in the German Ruhr area. Part I: reproducing the base state.], a description was given of the coupling of these models and of the validation of simulation results. In the present paper, a land use change scenario was implemented to mimic urban sprawl, relocating 12% of the urban population in the study domain to the green periphery. The resulting updated land use, population and employment density patterns were then used as input for traffic simulations, yielding an increase of total traffic volume by almost 17%. As a consequence, the domain-average simulated pollutant concentrations of ozone and particulate matter increased, though by a smaller amount, of approximately 4%. In a final step, population exposure to air pollution was calculated, both for the base case and the scenario simulations. A very slight domain-average exposure increase was found, of the order of a half percent. A compensating mechanism was identified, explaining this small figure. However, when stratifying the population into groups of individuals that were relocated to the urban periphery and those that were not, much larger exposure changes following urban sprawl emerged. Indeed, it was found that the relatively small proportion of relocated individuals benefited of a decrease of exposure to particulate matter by almost 13%, mainly because of their moving out of the most polluted areas; and that this came at the expense of an increase of exposure of 1.2% by the individuals not having moved. [Copyright &y& Elsevier]

This paper is Part II in a pair of papers that examines the results of the Community Multiscale Air Quality (CMAQ) model version 4.5 (v4.5) and discusses the potential explanations for the model performance characteristics seen. The focus of this paper is on fine particulate matter (PM2.5) and its chemical composition. Improvements made to the dry deposition velocity and cloud treatment in CMAQ v4.5 addressing compensating errors in 36-km simulations improved particulate sulfate (SO4 2−) predictions. Large overpredictions of particulate nitrate (NO3 −) and ammonium (NH4 +) in the fall are likely due to a gross overestimation of seasonal ammonia (NH3) emissions. Carbonaceous aerosol concentrations are substantially underpredicted during the late spring and summer months, most likely due, in part, to a lack of some secondary organic aerosol (SOA) formation pathways in the model. Comparisons of CMAQ PM2.5 predictions with observed PM2.5 mass show mixed seasonal performance. Spring and summer show the best overall performance, while performance in the winter and fall is relatively poor, with significant overpredictions of total PM2.5 mass in those seasons. The model biases in PM2.5 mass cannot be explained by summing the model biases for the major inorganic ions plus carbon. Errors in the prediction of other unspeciated PM2.5 (PMOther) are largely to blame for the errors in total PM2.5 mass predictions, and efforts are underway to identify the cause of these errors. [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 gives an overview of the set up, methodology and the obtained results of the CityDelta (phase 1 and 2) project. In the context of the Clean Air For Europe programme of the European Commission, the CityDelta project was designed to evaluate the impact of emission-reduction strategies on air quality at the European continental scale and in European cities. Ozone and particulate matter (PM) are the main components that have been studied. To achieve this goal, a model intercomparison study was organized with the participation of more than 20 modelling groups with a large number of modelling configurations. Two following main topics can be identified in the project. First, in order to evaluate their strengths and weaknesses, the participating models were evaluated against observations in a control year (1999). An accompanying paper will discuss in detail this evaluation aspect for four European cities. The second topic is the actual evaluation of the impact of emission reductions on levels of ozone and PM, with particular attention to the differences between large-scale and fine-scale models. An accompanying paper will discuss this point in detail. In this overview paper the main input to the intercomparison is described as well as the use of the ensemble approach. Finally, attention is given to the policy relevant issue on how to implement the urban air quality signal into large-scale air quality models through the use of functional relationships. [Copyright &y& Elsevier]

Abstract: This paper calculates PM2.5 mass concentrations (PM2.5) from CMAQ output quantities, and focuses on analysing the uncertainty caused by using mass concentrations of particles in the two CMAQ fine modes (PM i+j ) as approximations of PM2.5 in model evaluations. Conceptually, CMAQ fine mode and PM2.5 particles are different in terms of both concentrations and compositions. Quantitatively, the modelling results of the Pacific 2001 scenario at the Lower Fraser Valley shows that PM i+j and PM2.5 can be substantially or even extremely different, depending on relative humidity (RH) values. Under low RHs, PM i+j and PM2.5 generally correlate well and their quantitative differences are mostly moderate, although their maximum differences can still be substantial. Under high RHs, the correlation between PM i+j and PM2.5 deteriorates considerably and the quantitative differences increase dramatically. This is true whether the analysis is conducted on an all-component or a dry-component-only basis. Therefore, PM i+j could be used as an approximation of PM2.5 only on an average basis when RHs are low, but not under more general conditions. When compared with measured PM2.5 concentrations, the modelled concentrations of PM2.5 dry components (PM2.5,dry) performed much better than the modelled concentrations of fine mode dry components (PM i + j ,dry) for the modelling domain and period, since the overall positive bias of the modelled PM i + j ,dry was partially compensated by the lower PM2.5,dry values in comparison with PM i + j ,dry. In addition, by using PM2.5,dry, the model demonstrated a better skill in simulating 24-h moving averages (MA) of measured concentrations in comparison with simulating hourly concentrations. This is different from the case of using PM i + j ,dry, where the model could not clearly show a better skill in simulating 24-h MAs. Therefore, it is highly desirable to calculate PM2.5 values from CMAQ output and use them instead of PM i+j in model evaluations, especially under situations when RHs can be high. The method outlined in this paper can also be readily used for the calculations of PM concentrations at any cut-off diameter of interest, in addition to PM2.5 discussed here. [Copyright &y& Elsevier]

Abstract: In order to use an air quality modeling system with confidence, model performance must be evaluated against observations. While ozone modeling and evaluation is fairly developed, particulate matter (PM) modeling is still an evolving science. EPA has issued minimal guidance on PM and visibility model performance evaluation metrics, goals, and criteria. This paper addresses these issues by examining various bias and error metrics and proposes PM model performance goals (the level of accuracy that is considered to be close to the best a model can be expected to achieve) and criteria (the level of accuracy that is considered to be acceptable for modeling applications) that vary as a function of concentration and extinction. In this paper, it has been proposed that a model performance goal has been met when both the mean fractional error (MFE) and the mean fractional bias (MFB) are less than or equal to +50% and ±30%, respectively. Additionally, the model performance criteria has been met when both the MFE⩽+75% and MFB⩽±60%. Less abundant species would have less stringent performance goals and criteria. These recommendations are based upon an analysis of numerous PM and visibility modeling studies performed throughout the country. [Copyright &y& Elsevier]

Abstract: To ensure the efficiency of strategies to reduce air pollutants caused by road traffic, it is essential to evaluate the emission data to be used in emission calculation models. This basic approach of the BAB II project (BundesAutoBahn––federal highway) has been reached by simultaneous measurements of gaseous and particulate emissions on the windward and lee side perpendicular to a motorway. The differences between the measurements allows for the calculation of the emissions caused by traffic on the motorway. Measurements of CO, NO, NO x , CO2, O3, VOC, and particulates of high horizontal and vertical resolution were made. The experimental setup was made symmetric to the motorway, with 52m high towers on each side. The field phase took place from 1 to 25 May 2001 at the motorway A656 near Heidelberg, Germany. In cases of wind direction perpendicular to the motorway, the height of the plume caused by traffic emissions is detectable and the emissions released on the motorway can be calculated. With traffic census carried out simultaneously, the emission data can be estimated using emission factors given in the literature. Comparison between real-world traffic emissions and calculated emissions allows for an evaluation of the emissions calculated by the models. This paper gives an overview of BAB II, its measuring concept, the experimental setup, and the quality assurance and control program. It is shown in detail that the method of emission estimation by measurements of concentration differences between both sides of a motorway works quite well if the meteorological assumptions for determining real-world traffic emissions have been fulfilled. Detailed results in terms of measured and model-calculated emissions of gaseous and particulate species will be reported in a number of subsequent papers. [Copyright &y& Elsevier]

The Pacific 2001 Air Quality Study was a major program on ambient particulate matter carried out in the Lower Fraser Valley. This paper provides an overview of the study and gives accounts of the measurements in each major components of the field measurement program, including ground sites specifically targeted on traffic emissions, urban mixing, urban to rural transition, biogenic emissions from forest, and a mixture of urban and forest, and an airborne measurement program to link the ground based components. A summary of major findings is given in the paper, along with a general background for the papers in this issue. [Copyright &y& Elsevier]

An instantaneous secondary organic aerosol yield (IAY) is a different quantity than an overall aerosol yield (OAY), both qualitatively and quantitatively. Starting from Odum''s OAY equation, this paper derives and presents a new equation for the calculation of IAY values. As examples, the two-product form of the equation is applied to 10 representative aromatic and biogenic reactive organic gas (ROG) experiments in the literature. Values of IAY and OAY corresponding to wide ranges of organic aerosol mass concentrations (M0) are calculated using the new IAY equation in this paper and the original Odum''s OAY equation. These IAY and OAY values are shown through aerosol yield curves, which are the plots of IAY or OAY versus M0. Comparative analysis of the IAY and OAY curves shows that both IAY and OAY increase monotonically with M0 and they approach a common maximum value when M0 becomes very high. However, when M0 approaches zero, OAY also approaches zero but IAY approaches a fixed positive value for a given ROG. At any given M0 value, the value of IAY is always higher than that of OAY. The relative differences between IAY and OAY are especially significant under typical ambient and experimental M0 levels. IAY values can be orders of magnitude higher than OAY values and are much better representations of aerosol yields under these M0 levels. When it is used for secondary organic aerosols (SOA) estimation and modelling, the new IAY equation will significantly improve the underestimation of SOA formation caused by directly using the original OAY equation. More sophisticated SOA algorithms could also be developed based on the IAY equation presented here. [Copyright &y& Elsevier]

The COVID-19 pandemic altered the human mobility and economic activities immensely, as authorities enforced unprecedented lock down regulations. In order to reduce the spread of COVID-19, a complete lockdown was observed between 24 March – 31 May 2020 in Pakistan. This paper aims at investigating the PM 2.5 , AOD and column amounts of six trace gases (NO 2 , SO 2 , CH 4 , HCHO, C 2 H 2 O 2 , and O 3) by comparing periods of reduced emissions during lockdown periods with reference periods without emission reductions over Lahore, Pakistan. HYSPLIT cluster trajectory analyses were performed, which confirmed similar meteorological flow conditions during lockdown and reference periods. This provides confidence that any change in air quality conditions would be due to changes in human activities and associated emissions. The results show about 38% reduction in ambient surface PM 2.5 levels during the lockdown period. This change also positively correlated with MODIS DB and AERONET AOD data with a decrease of AOD by 42% and 35%, respectively. Reductions for tropospheric columns of NO 2 and SO 2 were about 20% and 50%, respectively during a semi lockdown period, while no reduction in the CH 4, C 2 H 2 O 2 , HCHO and O 3 levels occurred. During the lockdown period NO 2, O 3 and CH 4 were about 50%, 45% and 25% lower, respectively, but no reduction in SO 2 , C 2 H 2 O 2 and HCHO levels were noticed compared to the reference lockdown period for Lahore. HYSPLIT cluster trajectory analysis revealed the greatest impact on Lahore air quality through local emissions and regional transport from the east (agricultural burning and industry). • Drastic reductions in anthropogenic emissions due to COVID-19 lockdown regulations. • Anthropogenic emissions are substantial, and the magnitude of these reductions are important with regard air quality and climate. • No reduction C 2 H 2 O 2 and HCHO levels as Biomass burning for heating, crop residue burning, and biogenic activities were least effected by the COVID regulations. • No sharp reduction in SO 2 from fossil fuel power plants due to smaller decreases in energy demand during the COVID lock down period. [ABSTRACT FROM AUTHOR]

Particulate matter pollution is increasingly serious due to the acceleration of urbanization, which has an adverse effect on human health. In order to offset that the nonuniform spatial distribution of PM 2.5 and the short of long-term observational data from ground monitoring, optical products based on satellite retrieval, such as aerosol optical depth (AOD), had been the main data to estimate PM 2.5 concentration. This paper summaries the principle and progress of statistical models for PM 2.5 concentration estimation in recent decades. Specifically, we first reviews the characteristics and gap-filling methods of satellite-driven AOD products. Secondly, the auxiliary variables used to enhance the explanatory of PM 2.5 changes are introduced and their impact on fine particles is analyzed. Third and most important, we summarizes the statistical models of PM 2.5 concentration estimation, and discusses the model performance of each subtype separately (i.e., regression-based, machine learning-based and hybrid model). According to the summary and discussion of the above work, there are still problems and challenges that need to be paid attention and further improved. Finally, this paper also provides some feasible improvements about quality control of PM 2.5 measurements, maximize the role of PM 2.5 measurements in the model and the trend analysis of fine time scale PM 2.5. • A literature review of the statistical models for PM 2.5 estimation is provided. • Aerosol products and auxiliary variables used to estimation PM 2.5 are summarized. • The scale effect issues and the challenges of modeling in ground-level PM 2.5 estimation are analyzed. • The prospects of important scientific problems about PM 2.5 estimation are presented. [ABSTRACT FROM AUTHOR]

This paper illustrates the main results of a spatio-temporal interpolation process of PM 10 concentrations at daily resolution using a set of 410 monitoring sites, distributed throughout the Italian territory, for the year 2015. The interpolation process is based on a Bayesian hierarchical model where the spatial-component is represented through the Stochastic Partial Differential Equation (SPDE) approach with a lag-1 temporal autoregressive component (AR1). Inference is performed through the Integrated Nested Laplace Approximation (INLA). Our model includes 11 spatial and spatio-temporal predictors, including meteorological variables and Aerosol Optical Depth. As the predictors' impact varies across months, the regression is based on 12 monthly models with the same set of covariates. The predictive model performance has been analyzed using a cross-validation study. Our results show that the predicted and the observed values are well in accordance (correlation range: 0.79–0.91; bias: 0.22–1.07 μg / m 3 ; RMSE: 4.9–13.9 μg / m 3). The model final output is a set of 365 gridded (1 km × 1 km) daily PM 10 maps over Italy equipped with an uncertainty measure. The spatial prediction performance shows that the interpolation procedure is able to reproduce the large scale data features without unrealistic artifacts in the generated PM 10 surfaces. The paper presents also two illustrative examples of practical applications of our model, exceedance probability and population exposure maps. • Bayesian spatio-temporal hierarchical model for PM10 concentrations. • Model estimation and spatial prediction implemented using the INLA-SPDE approach. • Daily high-resolution PM10 concentrations map for a large domain like Italy. • Gaussian Markov random fields allow fast PM10 estimate on large spacetime domains. • Probability of exceedance and population exposure map as secondary model outcomes. [ABSTRACT FROM AUTHOR]

In this paper we illustrate the application of modern functional data analysis methods to study the spatiotemporal variability of particulate matter components across the United States. The approach models the pollutant annual profiles in a way that describes the dynamic behavior over time and space. This new technique allows us to predict yearly profiles for locations and years at which data are not available and also offers dimension reduction for easier visualization of the data. Additionally it allows us to study changes of pollutant levels annually or for a particular season. We apply our method to daily concentrations of two particular components of PM 2.5 measured by two networks of monitoring sites across the United States from 2003 to 2015. Our analysis confirms existing findings and additionally reveals new trends in the change of the pollutants across seasons and years that may not be as easily determined from other common approaches such as Kriging. [ABSTRACT FROM AUTHOR]

During a transition period from 2007 to 2009, the Chemical Speciation Network (CSN) altered its carbon sampling instrumentation and analysis methods to be more consistent with the Interagency Monitoring of Protected Visual Environments (IMPROVE) network. CSN and IMPROVE operated collocated samplers spanning the transition at eight sites. This paper compares elemental, organic, and total carbon (EC, OC, and TC, respectively) measurements at these collocated sites from 2005 through March 2016. Prior to the changes, the concentration measurements between the two networks had imprecisions of 17, 17, and 16% and biases of 0.22, −1.93, and −1.69 μg m −3 for EC, OC, and TC, respectively, with CSN measuring higher OC and TC than IMPROVE. After the sampling equipment and analytical methods were changed, the imprecision improved to 14, 12, and 10% and biases decreased to −0.02, −0.12, and −0.15 μg m −3 for EC, OC, and TC, respectively. The increased comparability, particularly the smaller biases, between the two networks facilitates comparisons between urban and rural aerosols in geospatial analyses. [ABSTRACT FROM AUTHOR]

The increasingly stringent emission regulations will mandate the retrofit of after-treatment devices for in-use diesel vehicles, in order to reduce their substantial particulate matter and nitrogen oxides (NO X ) emissions. In this paper, a combination of DOC (diesel oxidation catalyst), CDPF (catalytic diesel particulate filter) and SCR (selective catalytic reduction) retrofit for a heavy-duty diesel engine was employed to perform experiment on the engine test bench to evaluate the effects on the particulate matter emissions including particle number (PN), particle mass (PM), particle size distributions and nitrogenous compounds emissions including NO X , nitrogen dioxide (NO 2 )/NO X , nitrous oxide (N 2 O) and ammonia (NH 3 ) slip. In addition, the urea injection was also of our concern. The results showed that the DOC+CDPF+SCR retrofit almost had no adverse effect on the engine power and fuel consumption. Under the test loads, the upstream DOC and CDPF reduced the PN and PM by an average of 91.6% and 90.9%, respectively. While the downstream SCR brought about an average decrease of 85% NO X . Both PM and NO X emission factors based on this retrofit were lower than China-Ⅳ limits (ESC), and even lower than China-Ⅴ limits (ESC) at medium and high loads. The DOC and CDPF changed the particle size distributions, leading to the increase in the proportion of accumulation mode particles and the decrease in the percentage of nuclear mode particles. This indicates that the effect of DOC and CDPF on nuclear mode particles was better than that of accumulation mode ones. The upstream DOC could increase the NO 2 /NO X ratio to 40%, higher NO 2 /NO X ratio improved the efficiency of CDPF and SCR. Besides, the N 2 O emission increased by an average of 2.58 times after the retrofit and NH 3 slip occurred with the average of 26.7 ppm. The rate of urea injection was roughly equal to 8% of the fuel consumption rate. The DOC+CDPF+SCR retrofit was proved a feasible and effective measurement in terms of reducing particulate emissions and NO X simultaneously for in-use engine. However, it also resulted in higher N 2 O emission, NH 3 slip as well as urea injecting strategy problem which should be of further concern. [ABSTRACT FROM AUTHOR]

A state-of-the-art integrated chamber system has been developed for evaluating the performance of low-cost air quality sensors. The system contains two professional grade chamber enclosures. A 1.3 m 3 stainless-steel outer chamber and a 0.11 m 3 Teflon-coated stainless-steel inner chamber are used to create controlled aerosol and gaseous atmospheres, respectively. Both chambers are temperature and relative humidity controlled with capability to generate a wide range of environmental conditions. The system is equipped with an integrated zero-air system, an ozone and two aerosol generation systems, a dynamic dilution calibrator, certified gas cylinders, an array of Federal Reference Method (FRM), Federal Equivalent Method (FEM), and Best Available Technology (BAT) reference instruments and an automated control and sequencing software. Our experiments have demonstrated that the chamber system is capable of generating stable and reproducible aerosol and gas concentrations at low, medium, and high levels. This paper discusses the development of the chamber system along with the methods used to quantitatively evaluate sensor performance. Considering that a significant number of academic and research institutions, government agencies, public and private institutions, and individuals are becoming interested in developing and using low-cost air quality sensors, it is important to standardize the procedures used to evaluate their performance. The information discussed herein provides a roadmap for entities who are interested in characterizing air quality sensors in a rigorous, systematic and reproducible manner. [ABSTRACT FROM AUTHOR]