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Wind erosion from farmland is a significant contributor to atmospheric particulate matter, particularly PM 10 (atmospheric particulate matter with an aerodynamic diameter ≤ 10 μm), in regions with intensive agricultural activities such as the North China Plain (NCP). However, the specific impact of farmland dust emissions on regional air quality remains underexplored. This study integrated the Wind Erosion Prediction System (WEPS) model with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to investigate the effect of agricultural dust emissions on PM 10 concentrations in the NCP. Results indicated that wind erosion from agricultural fields in the NCP produced approximately 33.8 × 10 5 tons of PM 10 annually. These emissions are predominantly concentrated during the winter months from October to next April, with the highest emissions occurring in Hebei, Henan, and Shandong provinces. Using scenario experiments with the WRF-Chem model, we further quantified the contribution of agricultural dust emissions to atmospheric PM 10 concentrations. The findings revealed that farmland emissions contributed approximately 13% of atmospheric PM 10 concentrations during winter months (January and December), underscoring the significant role of agricultural activities in regional air pollution. This study highlights the importance of including farmland wind erosion into chemical transport models to more accurately understand and mitigate the effects of agricultural activities on atmospheric pollution in agricultural regions worldwide., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In this study, the levels of sulfur dioxide (SO 2 ) and nitrogen dioxide (NO 2 ) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO 2 and NO 2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO 2 was the highest in summer, while the mean indoor NO 2 concentration was the highest in winter in both areas. The ratio of NO 2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO 2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO 2 for the training set (RMSE = 2.108, MAE = 1.780, and R 2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R 2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO 2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study focuses on modeling air quality with an adaptive utilization of spatio-temporal information from multiple air quality monitoring stations under a multi-scale framework. To this end, it is necessary to consider different strategies to combine different methods to decompose the given series and to fuse multi-site information. Based on a systematic comparative analysis, we propose a novel multi-scale and multi-site modeling method named the multivariate empirical mode decomposition and spatial cosine-attention-based long short-term memory (MEMD-SCA-LSTM). The MEMD-SCA-LSTM first employs MEMD to decompose the multi-site air quality series into the scale-aligned components and then models the components at different scales. The high-frequency components are modeled by a novel SCA-LSTM, which employs LSTM with residual blocks to extract the temporal information and utilizes an attention mechanism based on the cosine similarity to adaptively extract interactions among different sites. Other relatively regular components are modeled by the LSTM. Empirical study on PM 2.5 in Hong Kong has demonstrated the effectiveness of fusing multi-site information using the spatial attention (SA) mechanism under the multi-scale framework with MEMD. The proposed MEMD-SCA-LSTM can improve the one-day ahead modeling performance with the mean absolute error and the root mean square error reduced over 10%, compared to the baseline modeling methods. For the two-day and three-day ahead performance, the MEMD-SCA-LSTM is still the best one. Furthermore, by visualizing the attention weights, we illustrate that our proposed SCA-LSTM can overcome some limitations of the traditional attention mechanisms and that the attention weights exhibit more informative patterns which could be used to analysis the transport of air pollutant between sites. The proposed modeling method is a general method, which is feasible and applicable to other pollutants in other cities or regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Ambient ozone (O 3 ) predictions can be very challenging mainly due to the highly nonlinear photochemistry among its precursors, and meteorological conditions and regional transport can further complicate the O 3 formation processes. The emission-based chemical transport models (CTM) are broadly used to predict O 3 formation, but they may deviate from observations due to input uncertainties such as emissions and meteorological data, in addition to the treatment of O 3 nonlinear chemistry. In this study, an innovative recurrent spatiotemporal deep-learning (RSDL) method with model-monitor coupled convolutional recurrent neural networks (ConvRNN) has been developed to improve O 3 predictions of CTM. The RSDL method was first used to build the ConvRNN within a 24-h scale to characterize the spatiotemporal relationships between the monitored O 3 data and CTM simulations, and then incorporated the recurrent pattern to achieve 72-h multi-site forecasts based on a pilot study over the Pearl River Delta (PRD) region of China. The results showed that the RSDL method predicted O 3 with high accuracy over this case study, with an increase of 27.54% in the correlation coefficient (R) average for all sites as well as an increase in R of 0.14-0.21 for all cities compared to CTM. Moreover, the regional distribution of CTM was further improved by the RSDL predictions with the data fusion technique, which greatly reduced the underpredictions of O 3 concentrations, particularly in high O 3 -level areas (concentrations >160 μg/m 3 ), with a 33.55% reduction in the mean absolute error (MAE)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Ambient nitrogen dioxide (NO 2 ) is derived from tailpipe vehicle emission and is linked with various of health outcomes. Personal exposure monitoring is crucial for accurate assessment of the associated disease risks. This study aimed to evaluate the utility of a wearable air pollutant sampler in determining the personal NO 2 exposure of school children for comparison with a model-based personal exposure assessment. We employed cost-effective, wearable passive samplers to directly measure personal exposure of 25 children (aged 12-13 years) in Springfield, MA to NO 2 over a five-day period in winter 2018. NO 2 levels were additionally measured at 40 outdoor sites in the same region using stationary passive samplers. A land use regression (LUR) model was developed based on the ambient NO 2 measures, with a good prediction performance (R 2  = 0.72) using road lengths, distance to highway, and institutional land area as predictor variables. Time-weighted averages (TWA), which incorporated the time-activity patterns of participants and LUR-derived estimates in children's primary microenvironments (homes, the school and commute paths), were calculated as an indirect measure of personal NO 2 exposure. Results indicated that the conventional residence-based exposure estimate approach, often used in epidemiological studies, differed from the direct personal exposure and could overestimate the personal exposure by up to 109 %. TWA improved personal NO 2 exposure estimates by accounting for the time activity patterns of individuals, a difference of 5.4 % ± 34.2 % was found for exposures compared to wristband measurements. Nevertheless, the personal wristband measurements exhibited a large variability due to the potential contributions from indoor and in-vehicle NO 2 sources. The findings suggest that exposure to NO 2 can be highly personalized based on individual activities and contact with pollutants in specific microenvironments, reaffirming the importance of measuring personal exposure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem model in its high-performance implementation to conduct 1-year simulations in 2015 at cubed-sphere C360 (∼25 km) and C48 (∼200 km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface fine particulate matter (PM 2.5 ) and nitrogen dioxide (NO 2 ), focusing on understudied regions. Our results indicate pronounced spatial heterogeneity at high resolution (C360) with large global population-weighted normalized root-mean-square difference (PW-NRMSD) across resolutions for primary (62-126%) and secondary (26-35%) PM 2.5 species. Developing regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSD for PM 2.5 in the Global South (33%), 1.3 times higher than globally. The PW-NRMSD for PM 2.5 for discrete southern cities (49%) is substantially higher than for more clustered northern cities (28%). We find that the relative order of sectoral contributions to population exposure depends on simulation resolution, with implications for location-specific air pollution control strategies.

Ambient fine particulate matter (PM 2.5 ) pollution is the principal environmental risk factor for health burdens in China. Identifying the sectoral contributions of pollutant emissions sources on multiple spatiotemporal scales can help in the formulation of specific strategies. In this study, we used sensitivity analysis to explore the specific contributions of seven major emission sources to ambient PM 2.5 and attributable premature mortality across mainland China. In 2016, about 60% of China's population lived in areas with PM 2.5 concentrations above the Chinese Ambient Air Quality Standard of 35 μg/m 3 . This percentage was expected to decrease to 35% and 39% if industrial and residential emissions were fully eliminated. In densely populated and highly polluted regions, residential sources contributed about 50% of the PM 2.5 exposure in winter, while industrial sources contributed the most (29-51%) in the remaining seasons. The three major sectoral contributors to PM 2.5 -related deaths were industry (247,000 cases, 35%), residential sources (219,000 cases, 31%), and natural sources (87,000, 12%). The relative contributions of the different sectors varied in the different provinces, with industrial sources making the largest contribution in Shanghai (65%), while residential sources predominated in Heilongjiang (63%), and natural sources dominated in Xinjiang (82%). The contributions of the agricultural (11%), transportation (6%), and power (3%) sources were relatively low in China, but emissions mitigation was still effective in densely populated areas. In conclusion, to effectively alleviate health burdens across China, priority should be given to controlling residential emissions in winter and industrial emissions all year round, taking additional measures to curb emissions from other sources in urban hotspots, and formulating air pollution control strategies tailored to local conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Air quality modeling (AQM) is often used to investigate gaseous pollution around industrial zones. However, this methodology requires accurate emission inventories, unbiased AQM algorithms and realistic boundary conditions. We introduce a new methodology for source apportionment of industrial gaseous emissions, which is based on a fuzzy clustering of ambient concentrations, along with a standard AQM approach. First, by applying fuzzy clustering, ambient concentration is expressed as a sum of non-negative contributions - each corresponding to a specific spatiotemporal pattern (STP); we denote this method as FUSTA (FUzzy SpatioTemporal Apportionment). Second, AQM of the major industrial emissions in the study zone generates another set of STP. By comparing both STP sets, all major source contributions resolved by FUSTA are identified, so a source apportionment is achieved. The uncertainty in FUSTA results may be estimated by comparing results for different numbers of clusters. We have applied FUSTA in an industrial zone in central Chile, obtaining the contributions from major sources of ambient SO 2 : a thermal power plant complex and a copper smelter, and other contributions from local and regional sources (outside the AQM domain). The methodology also identifies SO 2 episodes associated to emissions from the copper smelter., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO 2 dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Area (RJMA), which is known for its physiographic complexity. In the first step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well represented. In the second step, it was verified that all CALMET three meteorological configurations performed better for the most frequent wind speed classes so that the largest SO 2 concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In this paper, we develop a framework and metrics for estimating the impact of emission sources on regulatory compliance and human health for applications in air quality planning and life cycle impact assessment (LCIA). Our framework is based on a pollutant's characterization factor (CF) and three new metrics: Available Regulatory Capacity for Incremental Emissions (ARCIE), Source CF Ratio, and Activity Health Impact (AHI) Ratio. ARCIE can be used to assess whether a receptor location has capacity to accommodate additional source emissions while complying with regulatory limits. We present CF as a midpoint indicator of health impacts per unit mass of emitted pollutant. Source CF Ratio enables comparison of potential new-source locations based on human health impacts. The AHI Ratio estimates the health impacts of a pollutant in relation to the utilization of the source for each unit of product or service. These metrics can be applied to any pollutant, energy source sector (e.g., agriculture, electricity), source type (point, line, area), and spatial modeling domain (nation, state, city, region). We demonstrate these metrics through a case study of fine particulate (PM 2.5 ) emissions from U.S. corn stover harvesting and local processing at various scales, representing steps in the biofuel production process. We model PM 2.5 formation in the atmosphere using a novel reduced-complexity chemical transport model called the Intervention Model for Air Pollution (InMAP). Through this case study, we present the first area-source PM 2.5 CFs that address the recommendations of several LCIA studies to establish spatially explicit CFs specific to an energy source sector or type. Overall, the framework developed in this work provides multiple new ways to consider the potential impacts of air emissions through spatially differentiated metrics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

It has been reported that air quality models largely underestimate PM 2.5 concentrations during severe pollution events in China. In this study, the Models-3 Community Multi-scale Air Quality model (CMAQ) was employed to simulate PM 2.5 concentrations in May-June (non-heating period) and in November-December (heating period) of 2013 in northern China, with a particular focus on determining the causes of the underestimation. Modeling results reproduced the mass concentrations of PM 2.5 in approximately 50% of the non-heating and heating periods in Qingdao (referred to as the good periods), while the model performance was unsatisfactory during the remaining periods (the poor periods). In this respect, the overprediction of inorganic salts and the underprediction of organic matter in PM 2.5 canceled each other out and resulted in a good simulation of PM 2.5 concentrations during the good periods, whereas during poor periods, the bias of the planetary boundary layer height, wind direction, precipitation, and other factors caused inconsistencies between the simulated and observed PM 2.5 concentrations. Sensitivity studies showed that the underestimation of primarily emitted particles from local emissions was likely the main cause of PM 2.5 underpredictions during heavy haze days. Furthermore, our results implied that the assumption of the conditions of the gas-aerosol thermodynamic equilibria in the air quality model likely results in an overprediction of secondary PM 2.5 inorganic salts (SO 4 2- + NO 3 - + NH 4 + ) during clear days. In contrast, during heavy pollution or heavy haze days, high concentrations of air pollutants theoretically rapidly leads to gas/particle chemical equilibrium and no overprediction of SO 4 2- , NO 3 - , and NH 4 + concentrations. Nevertheless, the underestimation of primarily emitted particles from local sources during heavy haze days is yet to be explained and needs further investigation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Airborne trace elements (TEs) pose a notable threat to human health due to their toxicity and carcinogenicity, whereas their exposures and associated health risks in China remain unclear. Here, we present the first nationwide assessment of spatiotemporal exposure to 11 TEs in China by coupling a bottom-up emission inventory with a modified CMAQ model capable of TE simulation. Associated health risks of 11 TEs are then evaluated using a set of risk assessment models. Our results show that the CMAQ model could reasonably reproduce the spatiotemporal variations of 11 TEs in China compared to observations. We find significant but spatiotemporal-heterogeneous cancer risks associated with high-level exposure of TEs in China. Gridded cell concentrations of hexavalent chromium, arsenic, and nickel in eastern and central China usually exceed China's air quality standard limits, resulting in significant cancer risks that affected over 85% of the entire population in China in 2015. National annual mean population-weighted concentrations of 11 TEs decrease by 9.8-35.6% from 2012 to 2015, largely attributed to emission reduction from coal combustion. Our study provides critical insights for policymakers to implement stricter measures to alleviate health burdens and benefit relevant epidemiological research on airborne TEs.

Research and development has been a key part of the foundation for improvements in US air quality since the establishment of the Environmental Protection Agency (EPA) 50 years ago. Although the scientific accomplishments and advances over the course of EPA's history are often overshadowed by policy debates, much of the air pollution science and engineering we now consider to be routine did not exist when EPA was established. Many of the advances in air pollutant measurement, monitoring, modeling, and control were developed by EPA researchers or supported by EPA programs. The technical foundation built during EPA's early years has since given the Agency the scientific ability to respond quickly and effectively to unexpected and emerging issues. Equally important, EPA also developed approaches to conducting and presenting science in policy settings to ensure that the science was as objective and complete as possible and was communicated effectively. A look back at some of the accomplishments of EPA scientists and engineers provides a reminder that the cumulative effect of continual, incremental advances can result in large and lasting benefits to society.

Epidemiologic studies have found associations between fine particulate matter (PM 2.5 ) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM 2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM 2.5 concentrations in 2011, ii) potential changes in PM 2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM 2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM 2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m -3 ) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM 2.5 concentrations were estimated to decrease by about 1 μg m -3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m -3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM 2.5 concentrations less than 10 μg m -3 in 2011 and PM 2.5 improvements of about 2 μg m -3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average., (Published by Elsevier Inc.)

The WRF-Chem (Weather Research and Forecasting with Chemistry) model is implemented and validated against ground-based observations for meteorological and atmospheric variables for the first time in Northern Vietnam. The WRF-Chem model was based on HTAPv2 emission inventory with MOZCART chemical-aerosol mechanism to simulate atmospheric variables for winter (January) and summer (July) of 2014. The model satisfactorily reproduces meteorological fields, such as temperature 2 m above the ground and relative humidity 2 m above the ground at 45 NCHMF meteorological stations in January, but lower agreement was found in those simulations of July. PM 10 and PM 2.5 concentrations in January showed good temporal and spatial agreements to observations recorded at three CEM air monitoring stations in Phutho, Quangninh, and Hanoi, with correlation coefficients of 0.36 and 0.59. However, WRF-Chem model was underestimated with MFBs from - 27.9 to - 118.7% for PM 10 levels and from - 34.2 to - 115.1% for PM 2.5 levels. It has difficulty in capturing day-by-day variation of PM 10 and PM 2.5 concentrations at each station in July, but MFBs were in the range from - 27.1 to - 40.2% which is slightly lower than those in January. It suggested that further improvements of the model and local emission data are needed to reduce uncertainties in modeling the distribution of atmospheric pollutants. Assessment of biomass burning emission on air quality in summer was analyzed to highlight the application aspect of the WRF-Chem model. The study may serve as a reference for future air quality modeling using WRF-Chem in Vietnam.

Air quality data from Bogotá, Colombia, show high levels of particulate matter (PM), which often generate respiratory problems to the population and a high economic cost to the government. Since 2016, air quality in the city of Bogotá has been measured through the Bogota Air Quality Index (IBOCA) which works as an indicator of environmental risk due to air pollution. However, available technological tools in Bogotá are not enough to generate early alerts due to PM 10 and PM 2.5 . Currently, alerts are only announced once the measured PM values exceed a certain standard (e.g., 37 μ g/m 3 ), but not with enough anticipation to efficiently protect the population. It is necessary to develop an early air quality alert in Bogotá, in order to provide information that improves risk management protocols in the capital district. The purpose of this investigation is to validate the corrective alert presented on the 14th and 15th of February of 2019, through the WRF-Chem model under different weather conditions, using three different setups of the model to simulate PM 10 and PM 2.5 concentrations during two different climatic seasons and different resolutions. The results of this article generate a validation of two configurations of the model that can be used for the Environmental Secretary of the District (SDA) forecasts in Bogotá, Colombia, in order to contribute to the prediction of pollution events produced by PM 10 and PM 2.5 as a tool for an early alert system (EAS) at least 24 h in advance.

Great efforts have been made over the years to assess the effectiveness of air pollution controls in place in the metropolitan area of São Paulo (MASP), Brazil. In this work, the community multiscale air quality (CMAQ) model was used to evaluate the efficacy of emission control strategies in MASP, considering the spatial and temporal variability of fine particle concentration. Seven different emission scenarios were modeled to assess the relationship between the emission of precursors and ambient aerosol concentration, including a baseline emission inventory, and six sensitivity scenarios with emission reductions in relation to the baseline inventory: a 50% reduction in SO 2 emissions; no SO 2 emissions; a 50% reduction in SO 2 , NO x , and NH 3 emissions; no sulfate (PSO 4 ) particle emissions; no PSO 4 and nitrate (PNO 3 ) particle emissions; and no PNO 3 emissions. Results show that ambient PM 2.5 behavior is not linearly dependent on the emission of precursors. Variation levels in PM 2.5 concentrations did not correspond to the reduction ratios applied to precursor emissions, mainly due to the contribution of organic and elemental carbon, and other secondary organic aerosol species. Reductions in SO 2 emissions are less likely to be effective at reducing PM 2.5 concentrations at the expected rate in many locations of the MASP. The largest reduction in ambient PM 2.5 was obtained with the scenario that considered a reduction in 50% of SO 2 , NO x , and NH 3 emissions (1 to 2 μg/m 3 on average). It highlights the importance of considering the role of secondary organic aerosols and black carbon in the design of effective policies for ambient PM 2.5 concentration control.

In this paper, the influence of the horizontal model grid size and anthropogenic gridded emissions on the air quality forecast in Serbia was analyzed using the online-coupled Weather Research and Forecasting model with Chemistry (WRF-Chem). For that purpose, six simulations were performed. The model horizontal grid size was 20 × 20 km, 10 × 10 km, and 5 × 5 km. Two anthropogenic gridded emission inventories with different grid sizes were used, the global RETRO (REanalysis of the TROpospheric chemical composition) and the EMEP (The European Monitoring and Evaluation Program) for each model horizontal grid size. The modeled O 3 , NO 2 , and PM 10 concentrations in all six simulations were compared with the measured hourly data at the Serbian Environmental Protection Agency (SEPA) stations and an EMEP station during August 2016. The analysis shows that the influence of the model grid size is larger on PM 10 than on the O 3 and NO 2 concentration. The concentration of O 3 and PM 10 has a similar dependence on the emissions and the model grid size, while NO 2 has a larger dependence on the emission than on the model grid size. The simulation with the 5 × 5 km grid size and the EMEP anthropogenic emissions has optimal performance compared with the measured concentration. In this optimal simulation, the modeled O 3 concentrations overestimated the measured values at 3 stations and underestimated the measured values at 2 stations. At most stations, the modeled NO 2 concentrations underestimated the measured values. The modeled PM 10 concentrations highly underestimated the measured values at all stations.

The Eurasia Tunnel, a 5.4-km tunnel connecting the Asian and European sides of Istanbul, Turkey, was opened for operation in December 2016. This paper describes the air quality modeling that was conducted during the design phase of the structure, to evaluate the impact of the tunnel traffic on ambient air quality in the vicinity of the tunnel. The ventilation of the tunnel consists of longitudinal forced ventilation with vertical extraction through two stacks located near the Asian and European portals of the tunnel. The analysis was conducted using the AERMOD computer program for three pollutants CO, NO 2 , and PM 10 . Model results show that pollutants will rapidly disperse once released from the stack and will not affect air quality in the vicinity of the tunnel. The most critical parameters which controlled the ventilation system design were found to be NO 2 and PM 10 . Maximum concentrations are not expected to violate the pertinent Turkish and EU air quality standards. Overall, this analysis shows that the ventilation system is efficient for the dispersion of the pollutants.

To understand the public health benefits of the Clean Air Action Plan implemented in Shanghai from 2013-2017, the changes of the PM 2.5 exposure levels and related health and economic benefits were quantitatively evaluated by using air quality numerical modeling, health risk assessment, and environmental valuation methods. The results show that the proportion of the population exposed to a mean annual PM 2.5 concentration lower than or equal to 35 μg·m -3 has increased from 1.62% in the base year to 34.06% in the control year. The death risk attributable to ambient PM 2.5 exposure decreased from 15.2% in the base year to 11.9% in the control year. The total health benefits are approximately 11.841 billion RMB(95% CI:5.024-17.819 billion RMB), accounting for 0.55%(95% CI:0.23%-0.82%)of Shanghai's GDP in 2013. The implementation of the action plan has a positive effect on the protection of the health of the population. Health benefits in areas with dense populations and high PM 2.5 declines are more pronounced within the outer ring line of Shanghai City.

Atmospheric pollutants are strongly affected by transport processes and chemical transformations that alter their composition and the level of contamination in a region. In the last decade, several studies have employed numerical modeling to analyze atmospheric pollutants. The objective of this study is to evaluate the performance of the WRF-SMOKE-CMAQ modeling system to represent meteorological and air quality conditions over São Paulo, Brazil, where vehicular emissions are the primary contributors to air pollution. Meteorological fields were modeled using the Weather Research and Forecasting model (WRF), for a 12-day period during the winter of 2008 (Aug. 10th-Aug. 22nd), using three nested domains with 27-km, 9-km, and 3-km grid resolutions, which covered the most polluted cities in São Paulo state. The 3-km domain was aligned with the Sparse Matrix Operator Kernel Emissions (SMOKE), which processes the emission inventory for the Models-3 Community Multiscale Air Quality Modeling System (CMAQ). Data from an aerosol sampling campaign was used to evaluate the modeling. The PM 10 and ozone average concentration of the entire period was well represented, with correlation coefficients for PM 10 , varying from 0.09 in Pinheiros to 0.69 in ICB/USP, while for ozone, the correlation coefficients varied from 0.56 in Pinheiros to 0.67 in IPEN. However, the model underestimated the concentrations of PM 2.5 during the experiment, but with ammonium showing small differences between predicted and observed concentrations. As the meteorological model WRF underestimated the rainfall and overestimated the wind speed, the accuracy of the air quality model was expected to be below the desired value. However, in general, the CMAQ model reproduced the behavior of atmospheric aerosol and ozone in the urban area of São Paulo.

Facing the air pollution problems in China, emission control strategies have been implemented within the framework of national Five-Year Plan (FYP). According to the lack of post-assessment studies in the literature, this study assessed the effectiveness of the SO 2 emission control policies on power plants after the 11th FYP (2006-2010) by modeling emission control scenarios. The idealized emission control policy (the PS90 scenario with assumption of 90% SO 2 emission reduction from power plants) could reduce the SO 2 and SO 4 2- concentrations by about 51 and 14%, respectively, over the Yangtze River Delta region. While the actual emission control condition (the P2010 scenario based on the actual emissions from power plants in 2010) demonstrated that the actual reduction benefits were 30% of SO 2 and 9% of SO 4 2- . On the city scale, the P2010 scenario imposed positive benefits on Shanghai, Nanjing, Nantong, and Hangzhou with SO 2 reductions of about 55, 12, 30, and 21%, respectively, while an 11% increase of SO 2 concentration was found in Ningbo. The number of days exceeding China's National Ambient Air Quality Standard of Class I daily SO 2 concentration was estimated to be 75, 52, 7, 77, and 40 days for Shanghai, Nanjing, Nantong, Ningbo, and Hangzhou under the real SO 2 control condition (P2010). The numbers could be decreased by 16, 11, 2, 21, and 11% if the control effect reaches the level of the PS90 scenario. This study serves as a scientific basis to design capable enforcement of emission control strategies in China in the future national plans.

With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

This article describes the development and application of a streamlined air control and response modeling system with a novel response surface modeling-linear coupled fitting method and a new module to provide streamlined model data for PM2.5 attainment assessment in China. This method is capable of significantly reducing the dimensions required to establish a response surface model, as well as capturing more realistic response of PM2.5 to emission changes with a limited number of model simulations. The newly developed module establishes a data link between the system and the Software for Model Attainment Test-Community Edition (SMAT-CE), and has the ability to rapidly provide model responses to emission control scenarios for SMAT-CE using a simple interface. The performance of this streamlined system is demonstrated through a case study of the Yangtze River Delta (YRD) in China. Our results show that this system is capable of reproducing the Community Multi-Scale Air Quality (CMAQ) model simulation results with maximum mean normalized error<3.5%. It is also demonstrated that primary emissions make a major contribution to ambient levels of PM2.5 in January and August (e.g., more than 50% contributed by primary emissions in Shanghai), and Shanghai needs to have regional emission control both locally and in its neighboring provinces to meet China's annual PM2.5 National Ambient Air Quality Standard. The streamlined system provides a real-time control/response assessment to identify the contributions of major emission sources to ambient PM2.5 (and potentially O3 as well) and streamline air quality data for SMAT-CE to perform attainment assessments., (Copyright © 2015. Published by Elsevier B.V.)

Background: Ozone and PM₂.₅ are current risk factors for premature death all over the globe. In coming decades, substantial improvements in public health may be achieved by reducing air pollution. To better understand the potential of emissions policies, studies are needed that assess possible future health impacts under alternative assumptions about future emissions and climate across multiple spatial scales., Method: We used consistent climate-air-quality-health modeling framework across three geographical scales (World, Europe and Ile-de-France) to assess future (2030-2050) health impacts of ozone and PM₂.₅ under two emissions scenarios (Current Legislation Emissions, CLE, and Maximum Feasible Reductions, MFR)., Results: Consistently across the scales, we found more reductions in deaths under MFR scenario compared to CLE. 1.5 [95% CI: 0.4, 2.4] million CV deaths could be delayed each year in 2030 compared to 2010 under MFR scenario, 84% of which would occur in Asia, especially in China. In Europe, the benefits under MFR scenario (219000 CV deaths) are noticeably larger than those under CLE (109,000 CV deaths). In Ile-de-France, under MFR more than 2830 annual CV deaths associated with PM₂.₅ changes could be delayed in 2050 compared to 2010. In Paris, ozone-related respiratory mortality should increase under both scenarios., Conclusion: Multi-scale HIAs can illustrate the difference in direct consequences of costly mitigation policies and provide results that may help decision-makers choose between different policy alternatives at different scales., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Particulate matter is the main air pollutant in open pit mining areas. Preferred models that simulate the dispersion of the particles have been used to assess the environmental impact of the mining activities. Results obtained through simulation have been compared with the particle concentration measured in several sites and a coefficient of determination R(2)<0.78 has been reported. This result indicates that in the open pit mining areas there may be additional sources of particulate matter that have not been considered in the modeling process. This work proposes that the unconsidered sources of emissions are of regional scope such as the re-suspension particulate matter due to the wind action over uncovered surfaces. Furthermore, this work proposes to estimate the impact of such emissions on air quality as a function of the present and past meteorological conditions. A statistical multiple regression model was implemented in one of the world's largest open pit coal mining regions which is located in northern Colombia. Data from 9 particle-concentration monitoring stations and 3 meteorological stations obtained from 2009 to 2012 were statistically compared. Results confirmed the existence of a high linear relation (R(2)>0.95) between meteorological variables and particulate matter concentration being humidity, humidity of the previous day and temperature, the meteorological variables that contributed most significantly in the variance of the particulate matter concentration measured in the mining area while the contribution of the AERMOD estimations to the short term TSP (Total Suspended Particles) measured concentrations was negligible (<5%). The multiple regression model was used to identify the meteorological condition that leads to pollution episodes. It was found that conditions drier than 54% lead to pollution episodes while humidities greater than 70% maintain safe air quality conditions in the mining region in northern Colombia., (Copyright © 2014 Elsevier B.V. All rights reserved.)

When designing air pollution reduction policies, regional decision makers face a limited budget to choose the most efficient measures which will have impacts on several pollutants in different ways. RIAT+ is a regional integrated assessment tool that supports the policy maker in this selection of the optimal emission reduction technologies, to improve air quality at minimum costs. In this paper, this tool is formalized and applied to the specific case of a French region (Alsace), to illustrate how focusing on one single pollutant may exacerbate problems related to other pollutants, on top of conflicts related to budget allocation. In our case, results are shown for possible trade-offs between NO2 and O3 control policies. The paper suggests an approach to prioritize policy maker objectives when planning air pollution policies at regional scale., (Copyright © 2014 Elsevier B.V. All rights reserved.)

The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution. For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2K. Water vapor at 2-m is well-predicted but significant biases (>2 g kg(-1)) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0

This paper investigates the biases associated with the ground-level nitrogen dioxide (NO2) concentrations derived from the satellite Ozone Monitoring Instrument (OMI) NO2 data through comparisons with the modeling and the monitoring results for the state of California in 2008. The seasonal and annual average ground-level NO2 concentrations are both analyzed from the OMI using the local NO2 profile obtained from the GEOS-Chem simulation. The OMI-derived ground-level NO2 concentrations are then compared with the NO2 concentrations predicted by a GIS-Based Multi-Source and Multi-Box model (GMSMB) and the in-situ measurements, correlation coefficients among the three sets of results are all above 0.84 with an average slope of 0.81 ± 0.04. Particularly, various biases associated with the three data sets have been analyzed, and the OMI-derived NO2 concentrations and the GMSMB modeling results have been proven to be essential for assessing regional air pollutant exposure risks with the aid of the extensive remote sensing database., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Taichung Power Plant (TPP) is Taiwan's largest coal-fired power plant and is considered a major source of air pollution. During periods of deteriorating air quality, it is often required to reduce the load to reduce emissions. However, frequent power load shedding not only requires cost but also requires safety considerations. Therefore, it is necessary to explore the role that thermal power plant emissions play in air pollution in Taiwan. This study employed the Community Multiscale Air Quality modeling system with the brute-force method to analyze the PM2.5 concentration contributed by TPP. The results showed that among the various air basins in Taiwan, the Yun-Chi-Nan air basin (YCNAB), located to the south of TPP, was most severely affected by TPP's emissions, with an annual average affected concentration of 1.0 µg m−3 (3.3%). However, when serious PM2.5 pollution events (daily concentration > 70 µg/m3) occurred due to low wind speeds, the Central Taiwan air basin (CTAB), where TPP is located, became the area most severely affected by TPP's emissions. The low wind speed was caused by the interaction between the easterly wind field around Taiwan and Taiwan's north–south mountain ranges. When this happens, TPP's emissions would have a greater impact on the PM2.5 concentration at nearby stations in the CTAB and YCNAB, up to about 11%. Overall, on pollution days caused by low wind speeds, the largest TPP load reduction (40%) still had a certain effect as an emergency measure to improve the high PM2.5 pollution in central and southern Taiwan. [ABSTRACT FROM AUTHOR]

Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 μg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 μg m-3 in 2011 and PM2.5 improvements of about 2 μg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

Air pollution is a significant global problem that affects human health and the natural environment. This research article aims to provide a comprehensive review of the impacts of air pollution on health and ecosystems and present various analytical techniques used for its measurement and modeling. The study uses a systematic review method to identify and evaluate research articles that deal with air pollution, its impacts on health and ecosystems, analytical techniques, and modeling that are only available in English and published between 2012 and 2023. The findings outlined in this article contribute to an enhanced understanding of the detrimental effects of air pollution and provide insights into effective monitoring and mitigation strategies. In conclusion, by implementing effective policies, adopting clean technologies, promoting sustainable practices, and raising awareness, we can mitigate the impacts of air pollution and create a healthier and more sustainable future. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) have crucial influences on atmospheric chemistry. Accurate quantification of the VOC emissions is critical for air pollution research, especially when applying to air quality models. However, current bottom‐up emission inventories have biases, making observational constraints of VOC emissions necessary. We conducted concurrent VOC measurements in the Pearl River Delta (PRD) region during the summer of 2018 and found large discrepancies in the spatiotemporal variations of VOCs between observations and model simulations when using the priori VOC emission inventory (Multi‐resolution Emission Inventory for China). The normalized biases of total VOC concentrations ranged from −55% to 85% across the PRD cities in the study period. To improve the simulations, we constrained the anthropogenic VOC emissions based on their measured concentrations. The observation‐constrained VOC emissions showed clear diurnal variations and resolved the spatially‐concentrated priori emissions by reducing the high emissions by 15%–36% in the central PRD cities while elevating the sparse emissions in other cities. After employing the observation‐constrained VOC emissions, the model better reproduced the spatiotemporal variations of VOCs in the PRD region, alleviating the biases to −13%–13%. Furthermore, simulations of peak ozone (O3) concentrations were amended to reduce the mean normalized bias by 5%–12% on high O3 days. Our work has effectively combined VOC field measurements with air quality modeling to achieve better simulations of VOCs and O3. Besides, the observational‐constrained emissions are flexible for studying short‐term emission changes and their subsequent impacts on air quality. Plain Language Summary: Air quality models are important tools for studying the physical and chemical processes of air pollution. Model simulations rely heavily on the input emission profiles. Despite efforts to establish accurate and model‐ready emission inventories, biases persist, which further affects model results. Volatile organic compounds (VOCs) emitted from various anthropogenic sources are key precursors to ozone (O3) and secondary organic aerosols. In this study, we constructed an observational constraint to validate current VOC emission inventories against observations in nine cities. Constrained emission profiles revealed more pronounced spatiotemporal variations in VOC emissions across cities. Furthermore, since VOC emissions were constrained by observations, air quality model simulations of VOCs and O3 were significantly improved and more consistent with observations in terms of chemical composition, diurnal variation, and spatial variation. Our proposed approach is efficient and flexible in updating VOC emissions and is suitable for air quality modeling. The improved simulations of VOCs and O3 provide scientific support for further studies on the impacts of emission changes on air pollution and pollution control strategies. Key Points: Discrepancies found in spatiotemporal variations of volatile organic compounds (VOCs) between field measurements and model simulationsAnthropogenic VOC emissions in the Pearl River Delta region quantitatively estimated through observationsSimulations of spatiotemporal variations of VOCs and ozone improved by observation‐constrained emissions [ABSTRACT FROM AUTHOR]

Featured Application: The results presented in the article can be used to optimize activities in the field of road traffic management to improve air quality in urban street canyons. Constantly changing vehicle stock, modification of road infrastructure, and other conditions result in a need to update the knowledge on the effectiveness of individual traffic management strategies, which could form the basis for actions taken by local authorities to improve air quality in crowded city centers, especially in street canyons. The article presents research results that evaluate the theoretical effects of introducing select traffic reorganization scenarios in the example of four street canyons located in Krakow (Poland) that are different in terms of vehicle traffic volume and canyon geometry. These scenarios were based on a reduction in the average traffic speed, road capacity or the admission of cars meeting certain exhaust emission standards. The authors estimated changes in emissions of nitrogen oxides (NO, NO2 and total NOx) and particulate matter (PM10 and PM2.5) as well as investigated the effect of these changes on air quality in the canyons using the Operational Street Pollution Model (OSPM). Significant effects in terms of improving air quality were identified only in scenarios based on a significant reduction in traffic volume and the elimination of passenger cars and light commercial vehicles with internal combustion engines that did not meet the requirements of the Euro 4, Euro 5 or Euro 6 emission standards. For these scenarios, depending on the variant and canyon analyzed, the emission reduction was achieved at a level of approximately 36–66% for NO, 28–77% for NO2, 35–67% for NOx and 44–78% for both PM10 and PM2.5. The expected effect of improving air quality in individual street canyons for these substances was 15–44%, 5–14%, 11–36% and 3–14%, respectively. The differences obtained in the percentage reduction of emissions and pollutant concentrations in the air were the result of a relatively high background of pollutants that suppress the achieved effect of improving air quality to a large extent. [ABSTRACT FROM AUTHOR]

As the transportation sector continues to decarbonize through electrification, there is growing interest in quantifying potential tradeoffs in air pollution and health impacts due to potential excess emissions from the power sector. This study investigates air pollution and health impacts of policy-driven changes in the transportation sector and the associated power generation demand in the Northeast and Mid-Atlantic United States. Five illustrative scenarios were designed to capture the effects of different policies under the first mandatory market-based program to reduce greenhouse gases in the US power sector (Regional Greenhouse Gas Initiative—RGGI) and the Transportation and Climate Initiative (TCI). Considering future power generation with new renewable energy investments to meet demands from decarbonized transportation, the scenarios were framed using: 1. 2030 reference cases for both sectors and a hybrid TCI portfolio, 2. Departure from the reference cases defined by Pennsylvania included or not in RGGI, and 3. Power grid emissions estimated under clean energy standard (CES) policy and hybrid TCI portfolio. While the cross-sectoral policy effect on domain-wide concentrations is modest (max ΔPM _2.5 ∼ 0.06 μ g m ^3 , ΔNO _2 ∼ 0.3 ppbv, ΔO _3 ∼ 0.15 ppbv), substantial increases in Ohio and West Virginia were attributed to Pennsylvania joining RGGI. With CES enacted and Pennsylvania in RGGI, significant reductions are seen in average concentrations (max ΔPM _2.5 ∼ 1.2 μ g m ^3 , ΔNO _2 ∼ 1.1 ppbv, ΔO _3 ∼ 1.7 ppbv) except for Louisiana and Mississippi with corresponding disbenefits. When focusing exclusively on emissions reductions from transportation, the hybrid TCI portfolio had health benefits of 530 avoided adult deaths, and 46 000 avoided asthma exacerbations. With a ‘business as usual’ power grid, these benefits remain comparable and are mainly driven by NO _2 , followed by PM _2.5 and O _3 . However, if Pennsylvania joins RGGI, total health benefits and spatial distribution change substantially, with a large portion of adverse health impacts moving from TCI states to Ohio and West Virginia. The overall monetized impact of a CES scenario can substantially exceed the estimated average range of 66–69 Billion US$, depending on the interaction with transportation decarbonization strategies and other drivers of exposure.

Haze episode in Malaysia typically takes place during the dry monsoon season. As a result, high concentration of atmospheric particles was recorded primarily brought by transboundary air pollution from the neighbour country. Therefore, this study aims to evaluate and compare the level of particulate matter (PM10) at urban-industrial areas during the episodic haze episodes in Malaysia. Hourly PM10 concentration with the concentration of gaseous air pollutants such as NOx, NO2, SO2, CO and O3 and meteorological parameters (relative humidity, temperature, wind speed) at urban-industrial areas namely Shah Alam (Selangor), Nilai (Negeri Sembilan), Bukit Rambai (Melaka) and Larkin (Johor), during the haze episode in 1997, 2005, 2013 and 2015 were used for analysis. In this study, spatio-temporal and correlation analysis were used to provide an overview of the distribution pattern and examine the relationships between the gaseous air pollutants and meteorological parameters with PM10 concentration. From the descriptive statistics, it was observed that PM10 level for all study areas were skewed to the right (> + 1) indicating occurrences of extreme events. A significant peak of PM10 concentration for each year of haze events were observed to be started in June or during the southwest monsoon to the inter monsoon in October. The occurrence, duration and impact of 1997 haze was detected to be identical to the 2015 haze event that reached its peak in October. From the correlation analysis, PM10 concentration were strongly correlated to the CO concentration (r > 0.5) during High Particulate Event (HPE). Very weak relationship of PM10 level with meteorological parameters (r < 0.3) were observed. Interestingly, O3 level shows very strong correlation with the meteorological parameters during HPE. The findings provide comprehensive evaluation on PM10 level during the historic haze episodes, thus can help the authorities in developing policies and guidelines to effectively monitor and reduce the negative impact of haze events. [ABSTRACT FROM AUTHOR]

In this work, we identify the current atmospheric sulfur dioxide emissions of the Veracruz port, an important Mexican seaport experiencing rapid growth, and its influence on the surrounding areas. Sulfur dioxide emissions based on port activity, as well as meteorology and air quality simulations, are used to assess the impact. It was found that using marine fuel with low sulfur content reduces emissions by 88%. Atmospheric emission estimates based on the bottom-up methodology range from 3 to 7 Mg/year and can negatively impact air quality up to 3 km downwind. After evaluating different characteristics of vessels in CALPUFF, it was found that maximum sulfur dioxide concentrations ranging between 50 and 88 µg/m3 for a 24-h average occurred 500 m from the port. During 2019, five days had unsatisfactory air quality. The combination of a shallow planetary boundary layer, low wind speed, and large atmospheric emissions significantly degraded local air quality. [ABSTRACT FROM AUTHOR]

Ground-level ozone (O3) is a significant source of air pollution, mainly in most urban areas across the globe. Ground-level O3 is not emitted directly into the atmosphere. It results from photo-chemical reactions between precursors and is influenced by weather factors such as temperature. This study investigated the spatial and temporal analysis of ground-level ozone and analyzed the significant anthropogenic precursors and the weather parameters associated with ground-level ozone during daytime and nighttime at three cities in peninsular Malaysia, namely, Kuala Terengganu, Perai, and Seremban from 2016 to 2020. Secondary data were acquired from the Department of Environment (DOE), Malaysia, including hourly data of O3 with trace gases and weather parameters. The secondary data were analyzed using temporal analysis such as descriptive statistics, box plot, and diurnal plot as well as spatial analysis such as contour plot and wind rose diagram. Spearman correlation was used to identify the association of O3 with its precursors and weather parameters. The results show that a higher concentration of O3 during the weekend due to "ozone weekend effects" was pronounced, however, a slightly significant effect was observed in Perai. The two monsoonal seasons in Malaysia had a minimal effect on the study areas except for Kuala Terengganu due to the geographical location. The diurnal pattern of O3 concentration indicates bimodal peaks of O3 precursors during the peak traffic hours in the morning and evening with the highest intensity of O3 precursors detected in Perai. Spearman correlation analysis determined that the variations in O3 concentrations during day and nighttime generally coincide with the influence of nitrogen oxides (NO) and temperature. Lower NO concentration will increase the amount of O3 concentration and an increasing amount of O3 concentration is influenced by the higher temperature of its surroundings. Two predictive models, i.e., linear (multiple linear regression) and nonlinear models (artificial neural network) were developed and evaluated to predict the next day and nighttime O3 levels. ANN resulted in better prediction for all areas with better prediction identified for daytime O3 levels. [ABSTRACT FROM AUTHOR]

• NO 2 was modelled in high spatial resolution in an urban area. • Developed a data fusion model using citizen science data for hotspot identification. • Data fusion can address model overestimation or underestimation. • Citizen science raises community awareness of local air quality. • Significant implications for science and environmental health policy. Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality modelling by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO 2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO 2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the baseline dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO 2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health. [Display omitted] [ABSTRACT FROM AUTHOR]

Air pollution has a major impact on human health, especially in cities, and elevated concentrations of PMx are responsible for a large number of premature deaths each year. Therefore, the amount of PM10 in the air is monitored and forecasts are made to predict the air quality. In Poland, mainly deterministic models are used to predict air pollution. Accordingly, research efforts are being made to develop other models to forecast the ambient PM10 levels. The aim of the study was to compare the machine learning models for predicting PM10 levels in the air in the city of Lublin. The following machine learning models were used: Linear regression (LR), K-Nearest Neighbors Regression (KNNR), Support Vector Machine (SVM), Regression Trees (RT), Gaussian Process Regression Models (GPR), Artificial Neural Network (ANN) and Long Short-Term Memory network (LSTM). The collected data for three consecutive years (January 2017 to December 2019) were used to develop the models. In total, 19 parameters, covering meteorological variables and concentrations of several chemical species, were explored as potential predictors of PM10. The data used to build the models did not take into account the seasons. The algorithms achieved the following R2 values: 0.8 for LR, 0.79 for KNNR, 0.82 for SVM, 0.77 for RT, 0.89, 0.90 for ANN and 0.81 for LSTM. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. In this research, the ANN model performed slightly better than other models. Then, an ANN was used to train a network with five output neurons to predict the approximate level of PM10 at different time points (PM level at a given time, after 1 h, after 6 h, after 12 h and after 24 h). The results showed that the developed and tuned ANN model is appropriate (R = 0.89). The model created in this way can be used to determine the risk of exceeding the PM10 alert level and to inform about the air quality in the region. [ABSTRACT FROM AUTHOR]

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Constantly changing vehicle stock, modification of road infrastructure, and other conditions result in a need to update the knowledge on the effectiveness of individual traffic management strategies, which could form the basis for actions taken by local authorities to improve air quality in crowded city centers, especially in street canyons. The article presents research results that evaluate the theoretical effects of introducing select traffic reorganization scenarios in the example of four street canyons located in Krakow (Poland) that are different in terms of vehicle traffic volume and canyon geometry. These scenarios were based on a reduction in the average traffic speed, road capacity or the admission of cars meeting certain exhaust emission standards. The authors estimated changes in emissions of nitrogen oxides (NO, NO2 and total NOx) and particulate matter (PM10 and PM2.5) as well as investigated the effect of these changes on air quality in the canyons using the Operational Street Pollution Model (OSPM). Significant effects in terms of improving air quality were identified only in scenarios based on a significant reduction in traffic volume and the elimination of passenger cars and light commercial vehicles with internal combustion engines that did not meet the requirements of the Euro 4, Euro 5 or Euro 6 emission standards. For these scenarios, depending on the variant and canyon analyzed, the emission reduction was achieved at a level of approximately 36–66% for NO, 28–77% for NO2, 35–67% for NOx and 44–78% for both PM10 and PM2.5. The expected effect of improving air quality in individual street canyons for these substances was 15–44%, 5–14%, 11–36% and 3–14%, respectively. The differences obtained in the percentage reduction of emissions and pollutant concentrations in the air were the result of a relatively high background of pollutants that suppress the achieved effect of improving air quality to a large extent.

This study used AEDT 2.d to estimate the pollutant emissions at Montreal's Pierre Elliott Trudeau International Airport (YUL) for 2015 while quantifying the impact of the airport's taxi time and atmospheric conditions on aircraft emissions. Using the more airport-specific parameters available and ICAO standard values otherwise, the yearly emissions of NOx, HC, CO, PM10, SOx, and CO2 at YUL were, respectively, 7.64 102, 1.18 102, 1.33 103, 1.35 101, 6.77 101, and 2.31 105 tonnes/year. The results show that reducing aircraft taxi time by 31 % reduced aircraft emissions by 6 % (NOx) and 27 % (CO). Atmospheric conditions impact aircraft emissions of NOx, CO and HC with a seasonal effect. Summer conditions reduced NOx emissions by 5 % and increased CO emissions by 2 %, while winter conditions reduced HC and CO emissions by approximately 15 % and increased NOx emissions by 1 %. To further investigate the impact of atmospheric conditions, dispersion calculations of NOx and CO emissions were carried out using identical air-traffic and weather data from the warmest and coldest weeks in 2015. The results demonstrate that pollutant concentrations were higher during the winter and that pollutants were also dispersed further during the winter than the summer according to the dominant wind direction. A 1-h NOx and CO concentrations greater than 10 μg/m3 were found up to 24 km and 30 km, respectively, away during the winter compared to 14 km and 20 km during the summer. Yet, local air quality standards were satisfied as pollutant concentrations found outside the airport enclosure were below those standards. [ABSTRACT FROM AUTHOR]

Abstract Air pollution is associated to poor urban mobility conditions, as in the megacity of São Paulo. Three scenarios of policies of driving restrictions were simulated using the WRF/Chem model, to assess the impacts on ozone pollution and the consequences for environmental justice: urban toll, sustainable transition, and vehicle free zone. Results show that the urban toll leads to a decrease of 20 µg.m-3 in maximum 8-hour ozone averages and the prevention of six hours of exceedances of the air quality standard. Sustainable transition was the most promising scenario, with a decrease of 50 µg.m-3 and avoiding 22 hours of exceedances. The vehicle free zone did not show any advantages. For the implementation of adequate public transport systems, massive investments are required, in order to achieve better air quality with the greatest environmental justice possible, aiming towards the protection of human health along with a more democratic access to the urban spaces.

The potential impacts of climate change and future anthropogenic emissions on ozone levels in the USA are examined by linking global climate models to regional meteorological and air quality models during 3-year summer periods over the nine climate regions of the continental United States for three cases: (1) using 2016 meteorology and emissions (CTRL), (2) using 2050 meteorology and 2016 emissions (CASE1), and (3) using 2050 meteorology and 2050 emissions (CASE2). As climate change alone is expected to worsen ozone pollution and emission reductions are expected to reduce ozone concentrations, in this paper, the non-linear response of future ozone levels to both meteorological conditions and emissions was studied. The results show the well-known positive ozone correlation with surface temperature and negative ozone correlation with humidity in all regions. Climate change alone will increase future MDA8 ozone in the USA by 3.6 ppb. With climate change and policy intervention based on RCP 8.5, ozone levels will decrease 7.2 ppb on average for all climate regions in the USA. Furthermore, while climate change alone will double the number of stations violating the current National Ambient Air Quality Standard (NAAQS) for ozone in 2050, when policies are in effect, this number was reduced to 21 stations. The number of high-ozone days will also increase in climate change only case in all regions with an average of 5.7 extra high-ozone days which confirms previous studies. The results show that even with high-ozone precursor reductions, the ozone levels will still violate the current national ozone standard. Therefore, in order to meet the current ozone standard by 2050, more stringent climate and air pollution control policies for most regions in the USA are needed. [ABSTRACT FROM AUTHOR]

Air quality data from Bogotá, Colombia, show high levels of particulate matter (PM), which often generate respiratory problems to the population and a high economic cost to the government. Since 2016, air quality in the city of Bogotá has been measured through the Bogota Air Quality Index (IBOCA) which works as an indicator of environmental risk due to air pollution. However, available technological tools in Bogotá are not enough to generate early alerts due to PM10 and PM2.5. Currently, alerts are only announced once the measured PM values exceed a certain standard (e.g., 37 μ g/m3), but not with enough anticipation to efficiently protect the population. It is necessary to develop an early air quality alert in Bogotá, in order to provide information that improves risk management protocols in the capital district. The purpose of this investigation is to validate the correctivealert presented on the 14th and 15th of February of 2019, through the WRF-Chem model under different weather conditions, using three different setups of the model to simulate PM10 and PM2.5 concentrations during two different climatic seasons and different resolutions. The results of this article generate a validation of two configurations of the model that can be used for the Environmental Secretary of the District (SDA) forecasts in Bogotá, Colombia, in order to contribute to the prediction of pollution events produced by PM10 and PM2.5 as a tool for an early alert system (EAS) at least 24 h in advance. [ABSTRACT FROM AUTHOR]

Currently, there is an increasing interest in modeling the dispersion of atmospheric pollutants using computational fluid dynamics (CFD), to characterize the influence of the traffic-generated emissions on the temporal and spatial variability in air pollutant concentrations in the near-roadway environment. To advance in this task, we modeled the dispersion of total suspended particles (TSP), over a flat terrain, within a neutrally stratified and fully developed atmospheric boundary layer. We included the effect of turbulence and deposition on particle size distribution downstream. We found that TSP concentration downwind exhibits a single profile when expressed in terms of three dimensionless numbers: normalized concentration, normalized distance, and emission speed ratio. Using this generic character of the results, we determined the average short- and long-term TSP concentration, modeling successive short-term intervals in which it could be assumed a pseudo-steady-state behavior. Results exhibited correlation levels of R2 > 0.85 for daily and R2 > 0.94 for monthly averages when compared with measured TSP concentrations downwind two unpaved roadways. Results also showed that the implemented CFD model resolved the two main issues with Gaussian models (currently the most used air quality model): over-prediction of pollutant concentrations near the roadway and problems dealing with wind speeds < 1 ms−1. [ABSTRACT FROM AUTHOR]

A comprehensive emission inventory (EI) was conducted in Ho Chi Minh City (HCMC), the largest city of Vietnam rapid urbanization rate. In 2017, HCMC had 8.6 million inhabitants with a total of nine millions of private vehicles, 19 manufacturing and industrial zones, 30 industrial clusters, and numerous other factories and enterprises. All those sources could contribute to the high levels of emissions which caused the potentially negative impact on human health and environment. The aims of this study were (i) application bottom-up and top-down approaches to conduct a complete air emission inventory, (ii) development of spatial distribution of air emission, and (iii) estimation of emission forecast for HCMC by 2025 and 2030. A combination of bottom-up and top-down approaches was employed to conduct air pollution EI, in which EMISENS model was utilized to generate the EI for road traffic sources. The results showed that the motorcycles were the main reasons of emission in HCMC, contributing 90% of CO, 68% of non-methane volatile organic compounds (NMVOC), 63% of CH4, 41% of SO2, 29% of NOx, and 18% of patriculate matter (PM2.5). The emission forecasts for HCMC by 2025 and 2030 also were calculated based on the data of strategies and plans for socioeconomic development of HCMC. The results showed that the emissions of pollutants will increase around 30 to 50% by the year 2025 and from 40 to 65% by the year 2030. If the local government does not have any plan for the reduction of emissions (scenario of socioeconomic development as usual), the emissions will increase significantly. [ABSTRACT FROM AUTHOR]