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Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric pollutants, such as ozone (O 3 ). This study aims to elucidate global CO pollution in relation to health risks and the influence of natural events like wildfires. Utilizing artificial intelligence (AI) big data techniques, we developed a high-performance Convolutional Neural Network (CNN)-based Residual Network (ResNet) model to estimate daily global CO concentrations at a high spatial resolution of 0.07° from June 2018 to May 2021. Our model integrated the global TROPOMI Total Column of atmospheric CO (TCCO) product and reanalysis datasets, achieving desirable estimation accuracies with R-values (correlation coefficients) of 0.90 and 0.96 for daily and monthly predictions, respectively. The analysis reveals that the CO concentrations were relatively high in northern and central China, as well as northern India, particularly during winter months. Given the significant role of wildfires in increasing surface CO levels, we examined their impact in the Indochina Peninsula, the Amazon Rain Forest, and Central Africa. Our results show increases of 60.0%, 28.7%, and 40.8% in CO concentrations for these regions during wildfire seasons, respectively. Additionally, we estimated short-term mortality cases related to CO exposure in 17 countries for 2019, with China having the highest mortality cases of 23,400 (95% confidence interval: 0-99,500). Our findings highlight the critical need for ongoing monitoring of CO levels and their health implications. The daily surface CO concentration dataset is publicly available and can support future relevant sustainable studies, which is accessible at https://doi.org/10.5281/zenodo.11806178., 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 Elsevier Ltd. All rights reserved.)

Black carbon (BC) and brown carbon (BrC) are significant light-absorbing components of particulate matter that impact weather and climate. Biomass burning (BB) and biofuel (BF) emissions in Southeast Asia are key global sources of BC and BrC. This study utilizes the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model, integrating a BrC module for the first time, alongside the Global Fire Emissions Database Version 4, to assess the direct radiative effect (DRE) of BC and BrC in March 2015 over Southeast Asia. The novel BrC module parameterizes light absorption based on the BB BC to organic aerosol (OA) ratio in each model grid and time step, aligning better with smog chamber experiments than default coefficients. Results show that BC DRE affects clear sky net radiation at the top of the atmosphere, reaching 14.6 W/m 2 in Indochina and 5.1 W/m 2 in southern China, while BrC DRE reaches 1.9 W/m 2 . Additionally, BC and BrC DRE induce a cooling effect at the Earth's surface, with the maximum reduction of the clear sky downward shortwave radiation by -36.9 W/m 2 and -5.2 W/m 2 , respectively. BC and BrC lead to a tiny decrease (<0.1 °C) in surface temperature and an increase in upper air temperature. Surface O 3 concentration tends to slightly decrease (<1.0 ppbv) due to the DRE of BC and BrC. In comparison to studies using the default coefficient, this result contributes to a more nuanced understanding of the interactions between BC, BrC, and radiation., 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 Elsevier Ltd. All rights reserved.)

Investing in nonpotable water reuse (NPWR) is essential for circular urban water management. Existing research lacks methods to determine the number and capacities of NPWR plants (i.e., degree of decentralization) for large-scale applications in existing cities. We developed a spatial optimization framework in which the degree of decentralization emerges from the collective decisions of urban districts regarding where to send wastewater for reclamation and where to source water for nonpotable uses. We modified the genetic algorithm to optimize collective decisions with objectives including minimizing freshwater withdrawal, electricity consumption, and the cost of NPWR plants. Optimization results suggest an optimal number from one to eight among Pareto optimal solutions, with two to three being most common in Hong Kong. The cost-effective solutions suggest locations of NPWR plants in Kowloon and Hong Kong Island where NPWR demand is significant, while the electricity use for freshwater and seawater is high. The city could save about 6% freshwater and 29.4% seawater while consuming 20.7% more electricity. Overall, our spatial optimization framework provides a holistic evaluation of the optimal degree of decentralization for NPWR at the water-energy-cost nexus on an urban scale. Our findings serve as a benchmark to explore more energy-conscious planning strategies in Hong Kong.

Biomass burning (BB) is a major source of trace gases and particles in the atmosphere, influencing air quality, radiative balance, and climate. Previous studies have mainly focused on the BB emissions of carbon and nitrogen species with less attention on chlorine. Reactive chlorine chemistry has significant effects on atmospheric chemistry and air quality. However, quantitative information on chlorine emissions from BB, particularly the long-term trend and associated atmospheric impacts, is limited both on regional and global scales. Here, we report a long-term (2001-2018) high-resolution BB emission inventory for the major chlorine-containing compounds (HCl, chloride, and CH 3 Cl) in Asia based on satellite observations. We estimate an average of 730 Gg yr -1 chlorine emitted from BB activity in Asia, with China contributing the largest share at 24.2% (177 Gg yr -1 ), followed by Myanmar at 18.7% and India at 18.3%. Distinct seasonal patterns and significant spatial and interannual variability are observed, mainly driven by human-mediated changes in agricultural activity. By incorporating the newly developed chlorine emission inventory into a global chemistry-climate model (CAM-Chem), we find that the BB-chlorine emissions lead to elevated levels of HCl and CH 3 Cl (monthly average up to 2062 and 1421 parts per trillion by volume (pptv), respectively), subsequently resulting in noticeable changes in oxidants (up to 3.1% in O 3 and 17% in OH radicals). The results demonstrate that BB is not only a significant source of air pollutants but also of oxidants, suggesting a larger role of BB emissions in the atmospheric chemistry and oxidation process than previously appreciated. In light of the projected increase in BB activity toward the end of the century and the extensive control of anthropogenic emissions worldwide, the contribution of BB emissions may become fundamental to air quality composition in the future., (© The Author(s) 2024. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.)

Crop production is a large source of atmospheric ammonia (NH 3 ), which poses risks to air quality, human health and ecosystems 1-5 . However, estimating global NH 3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy 4,5 . Here we develop a machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH 3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr -1 , lower than previous estimates that did not fully consider fertilizer management practices 6-9 . Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH 3 emissions by about 38% (1.6 ± 0.4 Tg N yr -1 ) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH 3 emissions reductions of 47% (44-56%) for rice, 27% (24-28%) for maize and 26% (20-28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH 3 emissions could increase by 4.0 ± 2.7% under SSP1-2.6 and 5.5 ± 5.7% under SSP5-8.5 by 2030-2060. However, targeted fertilizer management has the potential to mitigate these increases., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Due to the non-linearity in ozone (O 3 ) formation, reducing the emission of nitrogen oxides (NO x ) may increase O 3 concentration. Given the counteractive O 3 response to NO x reduction, overall impact of air pollution controls can be ambiguous when the assessments focus on the changes in pollutant concentrations. In this study, a risk-based method was used to gauge the net effect of air pollution controls on mortality risk in the Beijing-Tianjin-Hebei (BTH) region during the 2022 Winter Olympics and Paralympics (WOP). This mega-event presents a unique opportunity to investigate the efficacy of deep cuts in pollutant emissions. Results show that O 3 concentrations greatly increased as nitrogen dioxide (NO 2 ) concentrations decreased in the BTH. Due to the active photochemical formations, O 3 became the dominant pollutant that affected human health during the WOP. Despite the substantial O 3 increases, the health benefits of NO 2 reductions overwhelmed the adverse health effects of O 3 increases in most regions of the BTH (at 81 out of 112 stations). After considering the impacts of particulate matter, the integrated health risk of air pollution mixtures declined almost everywhere in the BTH. Our results underscore the great necessity of changing the assessment paradigm of pollution control from using concentration-based methods to using risk-based methods. Together with the carbon neutrality policy, stringent control of NO x emission from combustion sources is a promising way to achieve synergistic control solutions for air pollution and climate change., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

Nitrogen dioxide (NO 2 ) plays a critical role in terms of air quality, human health, ecosystems, and its impact on climate change. While the crucial roles of the vertical structure of NO 2 have been acknowledged for some time, there is currently limited knowledge about this aspect in China. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary satellite instrument capable of measuring the hourly columnar amount of NO 2 . The study presented here introduces the use of mixing height for NO 2 in the atmosphere. A thorough examination of spatiotemporal variations in the mixing height of NO 2 was conducted using data from both the GEMS and ground-based air quality monitoring networks. A random forest model based on machine learning techniques was utilized to examine how meteorological parameters affect the mixing height of NO 2 . The results of our study reveal a notable seasonal fluctuation in the mixing height of NO 2 , with the highest values observed during the summer and the lowest values during the winter. Additionally, there was an increasing diurnal trend from early morning to mid-afternoon. Moreover, the study discovered elevated NO 2 mixing heights in the dry regions of northern China. The results also indicated a positive correlation between the mixing height of NO 2 and temperature and wind speed, while negative associations were found with relative humidity and air pressure. The machine learning model's predicted NO 2 mixing heights were in good agreement with the measurement-based outcomes, as evidenced by a coefficient of determination (R 2 ) value of 0.96 (0.84 for the 10-fold cross-validation). These findings emphasize the noteworthy influence of meteorological variables on the vertical distribution of NO 2 in the atmosphere and enhance our comprehension of the three-dimensional variations in NO 2 ., 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.)

Initial success has been achieved in Hong Kong in controlling primary air pollutants, but ambient ozone levels kept increasing during the past three decades. Volatile organic compounds (VOCs) are important for mitigating ozone pollution as its major precursors. This study analyzed VOC characteristics of roadside, suburban, and rural sites in Hong Kong to investigate their compositions, concentrations, and source contributions. Here we show that the TVOC concentrations were 23.05 ± 13.24, 12.68 ± 15.36, and 5.16 ± 5.48 ppbv for roadside, suburban, and rural sites between May 2015 to June 2019, respectively. By using Positive Matrix Factorization (PMF) model, six sources were identified at the roadside site over five years: Liquefied petroleum gas (LPG) usage (33%-46%), gasoline evaporation (8%-31%), aged air mass (11%-28%), gasoline exhaust (5%-16%), diesel exhaust (2%-16%) and fuel filling (75-9%). Similarly, six sources were distinguished at the suburban site, including LPG usage (30%-33%), solvent usage (20%-26%), diesel exhaust (14%-26%), gasoline evaporation (8%-16%), aged air mass (4%-11%), and biogenic emissions (2%-5%). At the rural site, four sources were identified, including aged air mass (33%-51%), solvent usage (25%-30%), vehicular emissions (11%-28%), and biogenic emissions (6%-12%). The analysis further revealed that fuel filling and LPG usage were the primary contributors to OFP and OH reactivity at the roadside site, while solvent usage and biogenic emissions accounted for almost half of OFP and OH reactivity at the suburban and rural sites, respectively. These findings highlight the importance of identifying and characterizing VOC sources at different sites to help policymakers develop targeted measures for pollution mitigation in specific areas., 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. Published by Elsevier B.V.)

Commuters are often exposed to relatively high air pollutant concentrations in public transport microenvironments (TMEs) because of their proximity to emission sources. Previous studies have mainly focused on assessing the concentrations of air pollutants in TMEs, but few studies have distinguished between the contributions of ambient air and internal sources to the exposure of commuters to air pollutants. The main objective of this study was to quantify the contributions of ambient air and internal sources to the measured particulate matter and gaseous pollutant concentrations in selected TMEs in Hong Kong, a high-rise, high-density city in Asia. A sampling campaign was conducted to measure air pollutant concentrations in TMEs in Hong Kong in July and November 2018 using portable air quality monitors. We measured the concentrations of each pollutant in different TMEs and quantified the infiltration of particulate matter into these TMEs. The double-decker bus had the lowest particulate matter concentrations (mean PM 1 , PM 2.5 , and PM 10 concentrations of 5.1, 9.5, and 13 μg/m 3 , respectively), but higher concentrations of CO (0.9 ppm), NO (422 ppb), and NO 2 (100 ppb). For all the TMEs, about half of the PM 2.5 were PM 1 particles. The Mass Transit Railway (MTR) subway system had a PM 2.5 /PM 10 ratio of about 0.90, whereas the PM 2.5 /PM 10 ratio was about 0.60-0.70 for the other TMEs. The MTR had infiltration factor estimates <0.4 for particulate matter, lower than those of the double-decker bus and minibus. The MTR had the highest contribution from internal sources (mean PM 1 , PM 2.5 , and PM 10 concentrations of 4.6, 13.4, and 15.8 μg/m 3 , respectively). This study will help citizens to plan commuting routes to reduce their exposure to air pollution and help policy-makers to prioritize effective exposure reduction strategies., 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.)

Nitrate (NO 3 - ) is often among the leading components of urban particulate matter (PM) during PM pollution episodes. However, the factors controlling its prevalence remain inadequately understood. In this work, we analyzed concurrent hourly monitoring data of NO 3 - in PM 2.5 at a pair of urban and suburban locations (28 km apart) in Hong Kong for a period of two months. The concentration gradient in PM 2.5 NO 3 - was 3.0 ± 2.9 (urban) vs. 1.3 ± 0.9 μg m -3 (suburban) while that for its precursors nitrogen oxides (NO x ) was 38.1 vs 4.1 ppb. NO 3 - accounted for 45 % of the difference in PM 2.5 between the sites. Both sites were characterized to have more available NH 3 than HNO 3 . Urban nitrate episodes, defined as periods of urban-suburban NO 3 - difference exceeding 2 μg m -3 , constituted 21 % of the total measurement hours, with an hourly NO 3 - average gradient of 4.2 and a peak value of 23.6 μg m -3 . Our comparative analysis, together with 3-D air quality model simulations, indicates that the high NO x levels largely explain the excessive NO 3 - concentrations in our urban site, with the gas phase HNO 3 formation reaction contributing significantly during the daytime and the N 2 O 5 hydrolysis pathway playing a prominent role during nighttime. This study presents a first quantitative analysis that unambiguously shows local formation of NO 3 - in urban environments as a driver for urban episodic PM 2.5 pollution, suggesting effective benefits of lowering urban NO x ., 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.)

Ambient fine particulate matter (PM 2.5 ) has severe adverse health impacts, making it crucial to reduce PM 2.5 exposure for public health. Meteorological and emissions factors, which considerably affect the PM 2.5 concentrations in the atmosphere, vary substantially under different climate change scenarios. In this work, global PM 2.5 concentrations from 2021 to 2100 were generated by combining the deep learning technique, reanalysis data, emission data, and bias-corrected CMIP6 future climate scenario data. Based on the estimated PM 2.5 concentrations, the future premature mortality burden was assessed using the Global Exposure Mortality Model. Our results reveal that SSP3-7.0 scenario is associated with the highest PM 2.5 exposure, with a global concentration of 34.5 μg/m 3 in 2100, while SSP1-2.6 scenario has the lowest exposure, with an estimated of 15.7 μg/m 3 in 2100. PM 2.5 -related deaths for individuals under 75 years will decrease by 16.3 and 10.5% under SSP1-2.6 and SSP5-8.5, respectively, from 2030s to 2090s. However, premature mortality for elderly individuals (>75 years) will increase, causing the contrary trends of improved air quality and increased total PM 2.5 -related deaths in the four SSPs. Our results emphasize the need for stronger air pollution mitigation measures to offset the future burden posed by population age.

As major air pollutants, nitrogen oxides (NO x , mainly comprising NO and NO 2 ) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NO x simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NO x emission, which integrates the simulation results of the 3D-CTM, surface NO 2 measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NO x emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NO x emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO 2 simulation using posterior NO x emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NO x emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NO x pollution., 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.)

Traditional air quality data have a spatial resolution of 1 km or above, making it challenging to resolve detailed air pollution exposure in complex urban areas. Combining urban morphology, dynamic traffic emission, regional and local meteorology, physicochemical transformations in air quality models using big data fusion technology, an ultra-fine resolution modeling system was developed to provide air quality data down to street level. Based on one-year ultra-fine resolution data, this study investigated the effects of pollution heterogeneity on the individual and population exposure to particulate matter (PM 2.5 and PM 10 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) in Hong Kong, one of the most densely populated and urbanized cities. Sharp fine-scale variabilities in air pollution were revealed within individual city blocks. Using traditional 1 km average to represent individual exposure resulted in a positively skewed deviation of up to 200% for high-end exposure individuals. Citizens were disproportionally affected by air pollution, with annual pollutant concentrations varied by factors of 2 to 5 among 452 District Council Constituency Areas (DCCAs) in Hong Kong, indicating great environmental inequities among the population. Unfavorable city planning resulted in a positive spatial coincidence between pollution and population, which increased public exposure to air pollutants by as large as 46% among districts in Hong Kong. Our results highlight the importance of ultra-fine pollutant data in quantifying the heterogeneity in pollution exposure in the dense urban area and the critical role of smart urban planning in reducing exposure inequities., Competing Interests:  , (Copyright © 2022. Published by Elsevier B.V.)

Photochemical regime for ozone (O 3 ) formation is complicated in the sense that reducing emission of nitrogen oxides (NO x ) may increase O 3 concentration. The lockdown due to COVID-19 pandemic affords a unique opportunity to use real observations to explore the O 3 formation regime and the effectiveness of NO x emission control strategies. In this study, observations from ground networks during the lockdowns were used to assess spatial disparity of the Ratio of Ozone Formation (ROF) for nitrogen dioxide (NO 2 ) reduction in the Greater Bay Area (GBA) of China. The health risk model from Air Quality Health Index (AQHI) system in Hong Kong was adopted to evaluate the risk tradeoffs between NO 2 and O 3 . Results show that the levels of O 3 increase and NO 2 reduction were comparable due to high ROF values in urban areas of central GBA. The ozone reactivity to NO 2 reduction gradually declined outwards from central GBA. Despite the O 3 increases, the NO x emission controls reduced the Integrated Health Risk (IHR) of NO 2 and O 3 in most regions of the GBA. When risk coefficients from the AQHI in Canada or the global review were adopted in the risk analyses, the results are extremely encouraging because the controls of NO x emission reduced the IHR of NO 2 and O 3 almost everywhere in the GBA. Our results underscore the importance of using a risk-based method to assess the effectiveness of emission control measures and the overall health benefit from NO x emission controls in the GBA., Competing Interests: 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., (© 2022 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.)

For the monitoring of urban air pollution, smart sensors are often seen as a welcome addition to fixed-site monitoring (FSM) networks. Due to price and simple installation, increases in spatial representation are thought to be achieved by large numbers of these sensors, however, a number of sensor errors have been identified. Based on a high-resolution modelling system, up to 400 pseudo smart sensors were perturbated with the aim of simulating common sensor errors and added to the existing FSM network in Hong Kong, resulting in 1200 pseudo networks for PM 2.5 and 1040 pseudo networks for NO 2 . For each pseudo network, population-weighted area representativeness (PWAR) was calculated based on similarity frequency. For PM 2.5 , improvements (up to 16%) to the high baseline representativeness (PWAR = 0.74) were achievable only by the addition of high-quality sensors and favourable environmental conditions. The baseline FSM network represents NO 2 less well (PWAR = 0.52), as local emissions in the study domain resulted in high spatial pollution variation. Due to higher levels of pollution (population-weighted average 37.3 ppb) in comparison to sensor error ranges, smart sensors of a wider quality range were able to improve network representativeness (up to 42%). Marginal representativeness increases were found to exponentially decrease with existing sensor number. The quality and maintenance of added sensors had a stronger effect on overall network representativeness than the number of sensors added. Often, a small number of added sensors of a higher quality class led to larger improvements than hundreds of lower-class sensors. Whereas smart sensor performance and maintenance are important prerequisites particularly for developed cities where pollutant concentration is low and there is an existing FSM network, our study shows that for places with high pollutant variability and concentration such as encountered in some developing countries, smart sensors will provide benefits for understanding population exposure., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Vertical transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) along a vertical column of flats has been documented in several outbreaks of coronavirus disease 2019 (COVID-19) in Guangdong and Hong Kong. We describe an outbreak in Luk Chuen House, involving two vertical columns of flats associated with an unusually connected two-stack drainage system, in which nine individuals from seven households were infected. The index case resided in Flat 812 (8th floor, Unit 12), two flats (813, 817) on its opposite side reported one case each (i.e., a horizontal sub-cluster). All other flats with infected residents were vertically associated, forming a vertical sub-cluster. We injected tracer gas (SF 6 ) into drainage stacks via toilet or balcony of Flat 812, monitored gas concentrations in roof vent, toilet, façade, and living room in four of the seven flats with infected residents and four flats with no infected residents. The measured gas concentration distributions agreed with the observed distribution of affected flats. Aerosols leaking into drainage stacks may generate the vertical sub-cluster, whereas airflow across the corridor probably caused the horizontal sub-cluster. Sequencing and phylogenetic analyses also revealed a common point-source. The findings provided additional evidence of probable roles of drainage systems in SARS-CoV-2 transmission., (Copyright © 2022 Elsevier B.V. All rights reserved.)

As the concentrations of primary components of fine particulate matter (PM 2.5 ) have substantially decreased, the contribution of secondary inorganic aerosols to PM 2.5 pollution has become more prominent. Therefore, understanding the variations in and characteristics of secondary inorganic aerosols is vital to further reducing PM 2.5 concentrations in the future. In this study, an ensemble back-propagation neural network model was built by combining 3D numerical models, observation data, and machine learning methods, to estimate the concentrations of secondary inorganic aerosols (SO 2- 4 , NO - 3 , and NH + 4 ) across the Greater Bay Area (GBA) in 2005 and 2015. The ensemble model provided a better estimation than the 3D numerical air quality model, with higher correlation coefficients (approximately 0.85) and lower root mean square errors. The model revealed that the concentrations of the SO 2- 4 , NO - 3 , and NH + 4 decreased by 1.91, 0.20, and 0.49 μg/m 3 , respectively, from 2005 to 2015. To investigate the oxidation and acidy of sulfate, the sulfur oxidation ratio (SOR), degree of sulfate neutralization (DSN), and particle neutralization ratio (PNR) were calculated and analyzed for 2005 and 2015 across the GBA region. The SOR slightly increased in summer, but decreased in other seasons in 2015, indicating the overall weaker sulfate chemical formation due to sulfur emission control measures. The increasing DSN and PNR indicated that more sulfate was neutralized due to reduced sulfur emission and increased ammonia availability. Our study suggests that more effort is needed to control ammonia emission to further reduce the concentrations of SO 2- 4 , NO - 3 , and NH + 4 across the GBA region in the future., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Ultrahigh-resolution air quality models that resolve sharp gradients of pollutant concentrations benefit the assessment of human health impacts. Mitigating fine particulate matter (PM 2.5 ) concentrations over the past decade has triggered ozone (O 3 ) deterioration in China. Effective control of both pollutants remains poorly understood from an ultrahigh-resolution perspective. We propose a regional-to-local model suitable for quantitatively mitigating pollution pathways at various resolutions. Sensitivity scenarios for controlling nitrogen oxide (NO x ) and volatile organic compound (VOC) emissions are explored, focusing on traffic and industrial sectors. The results show that concurrent controls on both sectors lead to reductions of 17%, 5%, and 47% in NO x , PM 2.5 , and VOC emissions, respectively. The reduced traffic scenario leads to reduced NO 2 and PM 2.5 but increased O 3 concentrations in urban areas. Guangzhou is located in a VOC-limited O 3 formation regime, and traffic is a key factor in controlling NO x and O 3 . The reduced industrial VOC scenario leads to reduced O 3 concentrations throughout the mitigation domain. The maximum decrease in median hourly NO 2 is >11 μg/m³, and the maximum increase in the median daily maximum 8-hr rolling O 3 is >10 μg/m³ for the reduced traffic scenario. When controls on both sectors are applied, the O 3 increase reduces to <7 μg/m³. The daily averaged PM 2.5 decreases by <2 μg/m³ for the reduced traffic scenario and varies little for the reduced industrial VOC scenario. An O 3 episode analysis of the dual-control scenario leads to O 3 decreases of up to 15 μg/m³ (8-hr metric) and 25 μg/m³ (1-hr metric) in rural areas., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2022 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Previous studies on long-term ozone (O 3 ) variations in China have reported inconsistent conclusions on the role of meteorological factors in controlling said variations. In this study, we used an observation-based decomposition model to conduct an up-to-date investigation of the effects of meteorological factors on the variations in nitrogen dioxide (NO 2 ) and O 3 concentrations in China in the summer from 2013 to 2020. The variations in NO 2 and O 3 concentrations after removing the major meteorological effects were then analyzed to improve our understanding of O 3 formation regimes. Ground measurements show that both NO 2 and O 3 concentrations decreased in eastern, central, and southeastern China (e.g., NO 2 and O 3 concentrations in Wuhan reduced by 4.3 and 6.2 ppb, respectively), which was not anticipated. Analyses of meteorological effects showed that reduced wind strength, decreased temperature, and increased relative humidity significantly reduced O 3 concentrations in eastern and central China (e.g., by 10.5 ppb in Wuhan). After removing the major meteorological effects, the O 3 trends were reversed in eastern and central China (e.g., increased by 4.9 ppb in Wuhan). The contrasting trends in NO 2 and O 3 concentrations suggest that their O 3 formations were sensitive to volatile organic compounds (VOC-limited regime). In southeastern China, both NO 2 and O 3 concentrations decreased, implying that the O 3 formation regimes changed to mixed sensitive or nitrogen oxide-limited (NO x -limited) regimes. The meteorological effects varied by region and may play a dominant role in controlling the long-term O 3 variation. Our results indicate that the attribution of O 3 variation to emission control without accounting for meteorological effects can be misleading., 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. Published by Elsevier B.V.)

Attributing sources of air pollution events by deploying an efficient observational network is an important and interesting problem in air quality control and forecast studies, but it is very challenging. In order to estimate the sensitivities of pollution events to emission sources, a comprehensive framework is built based on a horizontal 2-dimensional transport model and its adjoint in solving this problem. In an analysis of an idealized air pollution event of PM 2.5 over the region of North China, an objective function is defined to optimally estimate the initial concentrations and emission sources through a series of minimization procedures. Results by means of the 4-dimensional variational approach show that, with the optimal initial conditions and emission sources, the model can successfully forecast the pollution event in a few days. The optimal observing network based on sensitivity analysis takes only one third of the cost but greatly retains predictability skill compared to the full-grid observing system, while nearly no predictability skill is detectable if the same number of observational sites is randomly deployed. We evaluate air pollution predictability in the point of focusing on to what degree the root mean square errors between the modeled concentration and the targeted air pollution are limited by the optimal observational network. Results show that air pollution predictability in association with the optimal observational network is limited in the time scales about 6 days. With the high efficiency and in an economic fashion, such a sensitivity-based optimal observing system holds promise for accurately predicting an air pollution event in the targeted area once the adjoint and variational procedure of a realistic atmosphere model including transport and chemical processes is performed., 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.)

City air quality monitoring (AQM) network are typically sparsely distributed due to high operation costs. It is of the question of how well it can reflect public health risks to air pollution given the diversity and heterogeneity in pollution, and spatial variations in population density. Combing high-resolution air quality model, spatial population distribution and health risk factors, we proposed a population-health based metric for AQM representativeness. This metric was demonstrated in Hong Kong using hourly modelling data of PM10, PM2.5, NO2 and O3 in 2019 with grid cells of 45m * 48m. Individual and total hospital admission risks (%AR) of these pollutants were calculated for each cell, and compared with those calculated at 16 monitoring sites using the similarity frequency (SF) method. AQM Representativeness was evaluated by SF and a population-health based network representation index (PHNI), which is population-weighted SF over the study-domain. The representativeness varies substantially among sites as well as between population- and area-based evaluation methods, reflecting heterogeneity in pollution and population. The current AQM network reflects population health risks well for PM10 (PHNI = 0.87) and PM2.5 (PHNI = 0.82), but is less able to represent risks for NO2 (PHNI = 0.59) and O3 (PHNI = 0.78). Strong seasonal variability in PHNI was found for PM, increasing by >11% during autumn and winter compared to summer due to regional transport. NO2 is better represented in urban than rural, reflecting the heterogeneity of urban traffic pollution. Combined health risk (%ARtotal) is well represented by the current AQM network (PHNI = 1), which is more homogenous due to the dominance and anti-correlation of NO2 and O3 related %AR. The proposed PHNI metric is useful to compare the health risk representativeness of AQM for individual and multiple pollutants and can be used to compare the effectiveness of AQM across cities., Competing Interests: The authors have declared that no competing interests exist.

The current state-of-the-art three-dimensional (3D) numerical model for air quality forecasting is restricted by the uncertainty from the emission inventory, physical/chemical parameterization, and meteorological prediction. Forecasting performance can be improved by using the 3D-variational (3D-VAR) technique for assimilating the observation data, which corrects the initial concentration field. However, errors from the prognostic model cause the correction effects at the first hour to be erased, and the bias of the forecast increases relatively fast as the simulation progresses. As an emerging alternative technique, long short-term memory (LSTM) shows promising performance in air quality forecasting for individual stations and outperforms the traditional persistent statistical models. In this study, a new method was developed to combine a 3D numerical model with 3D-VAR and LSTM techniques. This method integrates the advantage of LSTM, namely its high-accuracy forecasting for a single station and that of the 3D-VAR technique, namely its ability to extend improvement to the whole simulation domain. This hybrid method can effectively improve PM 2.5 forecasting for the next 24 h, relative to forecasting with the 3D-VAR technique which uses the initial hour concentration correction. Results showed that the root-mean-square error and normalized mean error were decreased by 29.3% and 33.3% in the validation stations, respectively. The LSTM-3D-VAR method developed in this study can be further applied in other regions to improve the forecasting of PM 2.5 and other ambient pollutants., 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.)

Although the effects of meteorological factors on severe air pollution have been extensively investigated, quantitative decomposition of the contributions of meteorology and anthropogenic factors remains a big challenge. The novel coronavirus disease 2019 (COVID-19) pandemic affords a unique opportunity to test decomposition method. Based on a wind decomposition method, this study outlined an improved method to differentiate complex meteorological and anthropogenic effects. The improved method was then applied to investigate the cause of unanticipated haze pollution in China during the COVID-19 lockdown period. Results from the wind decomposition method show that weakened winds increased PM 2.5 concentrations in the Beijing-Tianjin area and northeastern China (e.g., by 3.19 μg/m 3 in Beijing). Using the improved decomposition method, we found that the combined meteorological effect (e.g., drastically elevated humidity levels and weakened airflow) substantially increased PM 2.5 concentrations in northern China: the most substantial increases were in the Beijing-Tianjin-Hebei region (e.g., by 26.79 μg/m 3 in Beijing). On excluding the meteorological effects, PM 2.5 concentrations substantially decreased across China (e.g., by 21.84 μg/m 3 in Beijing), evidencing that the strict restrictions on human activities indeed decreased PM 2.5 concentrations. The unfavorable meteorological conditions, however, overwhelmed the beneficial effects of emission reduction, causing the severe haze pollution. These results indicate that the integrated meteorological effects should be considered to differentiate the meteorological and anthropogenic effects on severe air pollution., Competing Interests: 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., (© 2021 Elsevier Ltd. All rights reserved.)

O 3 pollution had been worsening in mainland China in the past decade, posing significant human health challenges. The NO x control would trigger increasing O 3 concentrations in response to a series of released China emission reduction policies. This study used sensitivity analysis methodology to explore the effectiveness of integrated sectoral emission control policies that have been expanded throughout China. Air quality and synergistic health effects of O 3 and NO 2 were investigated to obtain an in-depth understanding of the O 3 control, especially under a VOC-limited regime. The findings demonstrated that although the NO x -titration effect triggered an increase in O 3 , the combined health effects of two pollutants tended to improve in most regions of China under a VOC-limited regime. The region-based annual average NO 2 concentrations exhibited a larger reduction in Hong Kong (HK) than in the Pearl River Delta Economic Zone (PRD EZ). The short-term measures led to substantial health benefits for Shenzhen and HK. The sectoral emission controls demonstrated a considerable health improvement for the major PRD EZ cities. Joint national control efforts confined the domain-wide health risks below the safety line in China. National cooperative efforts in China could avoid more than 1.5-2% of the emergency hospital admissions for cardiovascular and respiratory diseases attributed to NO 2 and O 3 exposure. The observed O 3 increases due to the NO x -titration effect for calculating the integral health effects of emission control on concentration reduction called for simultaneously strengthened controls on both NO x and VOC in areas subject to a VOC-limited regime., 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 © 2020 Elsevier Ltd. All rights reserved.)

PM 2.5 pollution has adverse health effects on humans. Urbanization and long-term meteorological variations play important roles in influencing the PM 2.5 concentration and its associated health effects. Our results indicate that the urbanization process can enhance the PM 2.5 concentration globally. The PM 2.5 -caused mortality density (deaths/100 km 2 ) is also positively correlated with the urbanization degree in both developed and developing countries. The results from machine learning technique revealed that the meteorology-driven variation in PM 2.5 -caused health burden has increased with the increase in the urbanization degree from 1980 to 2018, suggesting that residents living in urban areas are more vulnerable to experiencing unfavorable meteorological conditions (e.g. low wind speed and planetary boundary layer height). The maximum difference in PM 2.5 -caused mortality due to the variation in annual meteorological conditions (between 2013 and 1986) was 270 600 (196 800-317 900). Our findings indicate an urgent need to understand the driving force behind the appearance of unfavorable meteorological situations and propose suitable climate mitigation measures., 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 © 2020 Elsevier Ltd. All rights reserved.)

Recent studies have focused on the chemistry of tropospheric halogen species which are able to deplete tropospheric ozone (O 3 ). In this study, the effect of bromine and iodine chemistry on tropospheric O 3 within the annual cycle in Asia-Pacific is investigated using the CMAQ model with the newly embedded bromine and iodine chemistry and a blended and customized emission inventory considering marine halogen emission. Results indicate that the vertical profiles of bromine and iodine species show distinct features over land/ocean and daytime/nighttime, related to natural and anthropogenic emission distributions and photochemical reactions. The halogen-mediated O 3 loss has a strong seasonal cycle, and reaches a maximum of -15.9 ppbv (-44.3%) over the ocean and -13.4 ppbv (-38.9%) over continental Asia among the four seasons. Changes in solar radiation, dominant wind direction, and nearshore chlorophyll-a accumulation all contribute to these seasonal differences. Based on the distances to the nearest coastline, the onshore and offshore features of tropospheric O 3 loss caused by bromine and iodine chemistry are studied. Across a coastline-centric 400-km-wide belt from onshore to offshore, averaged maximum gradient of O 3 loss reaches 1.1 ppbv/100 km at surface level, while planetary boundary layer (PBL) column mean of O 3 loss is more moderate, being approximately 0.7 ppbv/100 km. Relative high halogen can be found over Tibetan Plateau (TP) and the largest O 3 loss (approximately 4-5 ppbv) in the PBL can be found between the western boundary of the domain and the TP. Halogens originating from marine sources can potentially affect O 3 concentration transported from the stratosphere over the TP region. As part of efforts to improve our understanding of the effect of bromine and iodine chemistry on tropospheric O 3 , we call for more models and monitoring studies on halogen chemistry and be considered further in air pollution prevention and control policy., 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 © 2020 Elsevier Ltd. All rights reserved.)

The novel coronavirus disease 2019 (COVID-19), which first emerged in Hubei province, China, has become a pandemic. However, data regarding the effects of meteorological factors on its transmission are limited and inconsistent. A mechanism-based parameterisation scheme was developed to investigate the association between the scaled transmission rate (STR) of COVID-19 and the meteorological parameters in 20 provinces/municipalities located on the plains in China. We obtained information on the scale of population migrated from Wuhan, the world epicentre of the COVID-19 outbreak, into the study provinces/municipalities using mobile-phone positioning system and big data techniques. The highest STRs were found in densely populated metropolitan areas and in cold provinces located in north-eastern China. Population density had a non-linear relationship with disease spread (linearity index, 0.9). Among various meteorological factors, only temperature was significantly associated with the STR after controlling for the effect of population density. A negative and exponential relationship was identified between the transmission rate and the temperature (correlation coefficient, -0.56; 99% confidence level). The STR increased substantially as the temperature in north-eastern China decreased below 0 °C (the STR ranged from 3.5 to 12.3 when the temperature was between -9.41 °C and -13.87 °C), whilst the STR showed less temperature dependence in the study areas with temperate weather conditions (the STR was 1.21 ± 0.57 when the temperature was above 0 °C). Therefore, a higher population density was linearly whereas a lower temperature (<0 °C) was exponentially associated with an increased transmission rate of COVID-19. These findings suggest that the mitigation of COVID-19 spread in densely populated and/or cold regions will be a great challenge., 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 © 2020 Elsevier B.V. All rights reserved.)

Wet deposition of non-sea-salt sulfate (nss-SO 4 2- ) and nitrate (NO 3 - ), derived from anthropogenic emissions of SO 2 and NO x , exerts adverse effects on ecosystems. In this work, an ensemble back-propagation neural network was proposed to estimate the long-term wet depositions of nss-SO 4 2- (2005-2017) and NO 3 - (2001-2014) over East Asia in 10 km resolution. The R 2 values for the 10-fold cross-validation of annual wet depositions of nss-SO 4 2- and NO 3 - were 0.90 and 0.85, respectively. The hotspots of the wet deposition of these two acidic species span southwestern, central, and eastern China. The molar ratio of NO 3 - to nss-SO 4 2- increased in 10 out of 12 analyzed East Asian countries from 2005 to 2014, which indicates that the acidity in rainwater shifts from the sulfur type to nitrogen type over most of the regions. The wet deposition on the four ecosystems (forest, grassland, cropland, and freshwater body) was also analyzed. Results showed that the nss-SO 4 2- wet deposition on 25.5% of freshwater bodies in 2015 and NO 3 - wet deposition on 21.7% of grassland in 2014 exceeded the ecosystem empirical critical loads (25 kg/ha sulfate and 2 kg N/ha) in East Asia. Thus, more stringent and regionally collaborative sulfur and nitrogen emission-control measures are urgently needed to protect the ecosystem of East Asia.

Problems with PM 2.5 pollution in the Pearl River Delta (PRD) have been significantly reduced since the Chinese government released a series of emission control policies including the strengthened controls in the 13 th Five-Year Plan. This study assessed the efficacy of emission control measures using the Community Multiscale Air Quality (CMAQ) model to provide data-driven support to government decision making, which is becoming increasingly important. This study aimed to quantitatively evaluate the integrated results of proposed policies for controlling PM 2.5 concentrations. Accordingly, the regional 2015 emission inventory was modified with recently released government data for the PRD, and scenarios for four dynamical emission-reduction policies (S1-S4) were explored. The results show that all four proposed control measures can help to reduce PM 2.5 concentrations throughout Hong Kong (HK), Macao, and the PRD economic zone (PRD EZ) by 2020. In all cases, reductions in PM 2.5 concentrations were larger over PRD EZ than over HK. For HK, the predicted annual concentrations of PM 2.5 were less than 20 μg/m 3 for S1-S3 and less than 15 μg/m 3 for S4. For Macao, the predicted annual concentrations of PM 2.5 were less than 25 μg/m 3 for S1 and less than 15 μg/m 3 up to S3. Regionally, HK had the lowest PM 2.5 levels, and the area around Foshan had the highest. Controlling the sources of air pollution (i.e., industry, transport, power production, and other sources) within PRD can get most of the PRD EZ region to below 35 μg/m 3 . Similar national air quality management efforts could reduce PM 2.5 levels to less than 25 μg/m 3 in the PRD EZ and less than 15 μg/m 3 in HK. Control measures in S1 led to significant improvement in Shenzhen and HK, but the S3 option brought the greatest improvement for PRD EZ and Macao. The S4 policy option led to substantial reductions, particularly for HK., 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 © 2020 Elsevier Ltd. All rights reserved.)

Human exposure to PM 2.5 , represented by population-weighted mean PM 2.5 concentration (c ρ ), declines under three conditions: (1) mean PM 2.5 concentration declines, (2) PM 2.5 concentration within urban areas goes through more of a decrease than within rural areas, or (3) city planning relocates people into cleaner areas. Decomposing these effects on human exposure is essential to guide future environmental policies. The lack of ground PM 2.5 observations limits the assessment of human exposure to PM 2.5 over China. This study proposed a novel diagnostic framework using satellite observations to decompose the variation in c ρ resulting from change in the mean PM 2.5 concentration, spatial difference in PM 2.5 change, and demographic change. In this framework, we decomposed c ρ into mean PM 2.5 concentration (c 0 ) and pollution-population-coincidence induced PM 2.5 exposure (PPCE). We then used this framework to decompose the variation in c ρ over China within three recent Five-Year Plans (FYPs) (2001-2015). The results showed that the decline in c 0 reduced c ρ in most provinces within the eleventh and twelfth FYPs. The spatial difference in PM 2.5 change reduced the PPCE and c ρ in most provinces within the tenth and twelfth FYPs, with the most substantial reduction rate of -3.64 μg m -3 ·yr -1 in Tianjin within the twelfth FYP. Rural-to-urban migration resulting from rapid urbanization, however, increased the PPCE and c ρ (by as much as 0.22 μg m -3 ·yr -1 ) in all provinces except Taiwan within all three FYPs. The demographic change reduced c ρ in Taiwan because of the migration of population into less polluted areas. To better reduce human exposure, it is recommended that control efforts further target populous residential areas and urbanization planning relocates people into less polluted areas. Our decomposition framework paves a new way to decompose the human exposure to other air pollutants in China and other regions., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Portable light-scattering aerosol monitors (PLSAMs) can supplement existing air quality monitoring networks through measuring air pollutant exposure concentrations at high spatiotemporal resolution. However, data collected by PLSAMs are often subject to the simplicity of measurement principle which may lead to errors compared to the regulatory data observed at fixed-site air quality monitoring stations. The main objective of this study was to develop a feasible experimental framework to assess the influence of key factors (e.g., relative humidity (RH)) on the performance of PLSAMs in the real-world conditions. Following the proposed framework, the accuracy and precision of the TSI DustTrak aerosol monitor were evaluated through side-by-side comparison with the stationary reference instruments (SRIs) while taking characteristics of particles, RH, and the concentration range into consideration. DustTrak generally demonstrated low accuracy but high precision in measuring PM 2.5 concentrations at the two selected stations. Three calibration models between DustTrak and the SRIs were used to bias correct the DustTrak PM 2.5 measurements. The RH-adjusted linear regression calibration method led to better calibration results than the simple linear regression method and the RH-adjusted empirical method, with CV R 2 values higher than 0.97, root mean square error less than 1.0 μg/m 3 , and accuracy values at 3% for two DustTraks. The proposed experimental framework can be extended to field calibration of various types of PLSAMs, and the obtained calibration results can promote a more accurate investigation of particle air pollution using these PLSAMs., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

PM 2.5 is one of the most notorious ambient pollutants in the Pearl River Delta (PRD) region during episodic conditions. In this work, the Comprehensive Air Quality Model with extension (CAMx) was used together with the Particulate Source Apportionment Technology (PSAT) module to analyze the influences of different sources on PM 2.5 concentration in the PRD region under different synoptic patterns (sea high pressure, sub-tropical high pressure and equalizing pressure field). The result shows that the PM 2.5 concentration increases to different degrees under the three synoptic patterns. The emissions outside the PRD region contribute more than 54% under episodic conditions. The source category contribution varies little under different synoptic patterns. Area (46%), mobile (21%) and industry point source (16%) are the major contributors over the three episodic cases. The regional source contributions (from other cities within the PRD) to Foshan, Zhongshan and Zhaoqing are larger and can reach up to 33%. People living in the PRD region are more exposed to pollutants produced from the area and mobile sources. About 80% of the population is exposed to PM 2.5 levels exceeding the IT-3 standard during the pollution episodes., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The below-cloud washout (BCW) effect on PM 2.5 concentration during periods of rain is still a subject of debate. Existing BCW schemes for PM 2.5 have large deficiencies that influence its simulation in 3D chemical transport models (CTMs). In this study, a 7-year dataset with high temporal resolution (in minutes) sampled from a pristine rural site is used to calculate the BCW coefficient during the rain events. The data used for the BCW coefficient calculation cover a wide range of rain intensity from 2 mm h -1 to 60 mm h -1 . The BCW coefficient linearly correlates with the rain intensity, with a correlation coefficient of 0.82. The coefficient has a magnitude of 10 -5 to 10 -4 s -1 when the rain intensity ranges from 1 to 40 mm h -1 . After implementing the updated BCW scheme into the Comprehensive Air Quality Model with Extensions (CAMx) model, the performance of PM 2.5 simulation improves for the two months of heavy rain. Apart from the CAMx model, our scheme can be easily implemented into other 3D CTMs to improve PM 2.5 simulation during rainy days. The BCW effect can clean around 10-40% of the PM 2.5 over our study region, which can help to reduce the PM 2.5 exposure level for residents, and the health burdens caused by this pollutant can thus be reduced. Rainmaking is a potential way to decrease PM 2.5 concentration, but it cannot be the key method to reduce the PM 2.5 level to the standard during episodic cases (e.g., >200 μg/m 3 )., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

During China's 11th Five Year Plan (FYP) and 12th FYP (2006-2015), a series of air pollution control measures was implemented in the Pearl River Delta (PRD) region. Therefore, it is vital to determine how the concentration and sources of fine particulate matter (PM 2.5 ) in this region changed between 2006 and 2015. In this work, using 2006 and 2015 emission inventories, the concentration and source apportionment of PM 2.5 were simulated using the Weather Research and Forecast - Comprehensive Air Quality Model with Extensions (WRF-CAMx) for January, April, July and October in the PRD region. The PM 2.5 in 10 cities and the contributions made by sources in six major categories were tracked using the Particulate Source Apportionment Technology (PSAT) module. The results showed that the PM 2.5 concentration was lower across the entire PRD region in the 2015 emission scenario than in the 2006 scenario, and that the degree of this reduction exceeded 40 μg/m 3 in some places. The PM 2.5 contributed by mobile emissions decreased the most, especially in Guangzhou, Foshan and Shenzhen, where mobile contributions decreased from 15.0, 17.9 and 13.0 μg/m 3 in 2006 to 2.6, 3.1 and 4.1 μg/m 3 in 2015, respectively. The PM 2.5 contributed by power plants also decreased, and in Dongguan and Guangzhou, the extent of this reduction reached 2.5 and 3.4 μg/m 3 respectively. However, due to an increase in industrial production and population size, the PM 2.5 from industrial point sources and area sources also increased between 2006 and 2015 in some of the cities. Investigation of the source apportionment for city centers yielded similar results. In addition to emissions within the PRD region, outside-PRD non-local contribution is still an important PM 2.5 contributor. Hence, more stringent policies for controlling industrial and area sources and deepening province-to-province cooperation are urgently needed as the next step in PM 2.5 control., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In this study, the concept of air parcel residence time was raised and the APRT was investigated to study its potential application in air pollution prevention and control in the Pearl River Delta (PRD) region. The APRT in the PRD region was defined as the total period for which an air parcel stays within the PRD region. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to calculate the hourly APRT in 2012, 2014, and 2015 based on forward trajectories from 16,720 starting locations. The seasonal APRT results revealed that long APRT was mainly distributed in southern PRD in the summer half year, but in northeastern PRD in the winter half year. This is related to the prevailing wind directions in the summer and winter monsoons. Moreover, the comparison of APRT in different years revealed that the dispersion condition was relatively poor in fall in 2012 and throughout 2014 but was relatively favorable in 2015, which also corresponded to the pollutant concentrations. The APRT calculated from regional air pollution days indicated that the emission reduction strategy should be implemented in the key areas, namely the eastern and central Guangzhou, western Huizou, and the border between Foshan and Jiangmen, and the construction of new factories should not be allowed in these areas. Compared to the APRT, which was investigated to trace the air pollution source, population exposure to air parcels (PEAP) was investigated to orient the influence of path-and-time-weighted sources to population. Consequently, a high PEAP was found to be distributed mainly in the central Guangzhou and Shenzhen and scattered in other urban areas., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Variations in aerosol characteristics play an essential role in satellite remote sensing of PM 2.5 concentrations. The lack of measurement of aerosol characteristics, however, limits the assessment of their effects. This study presented an observation-based model that directly considered the effects of aerosol characteristics. In this model, we used an integrated humidity coefficient (γ') and an integrated reference value ( K) to delineate the effects of aerosol characteristics. We then investigated the effects of the long-term variations in aerosol characteristics on satellite remote sensing of PM 2.5 concentration in Hong Kong from 2004 to 2012. The results show that the γ' value peaked in 2009 because the percentages of highly hygroscopic components (e.g., sulfate and nitrate) in aerosols reached their peaks. The K value increased from 2004 to 2011 because of the increasing percentages of strong light-extinction components (e.g., organic matter) and the decreasing fine mode fraction in aerosols. The accuracy of PM 2.5 retrieval improved greatly after accounting for the long-term variations in aerosol characteristics (e.g., correlation coefficient increased from 0.56 to 0.80). The results underscore the need to incorporate the variations in aerosol characteristics in the PM 2.5 estimation models.

In this study, the trend of PM 2.5 concentrations and its adverse health effects in China from 2001 to 2017 are estimated utilizing 1-km high-resolution annual satellite-retrieved PM 2.5 data. PM 2.5 concentrations for most of the provinces/cities remained stable from 2001 to 2012; however, following the issue of the Air Pollution Prevention and Control Action Plan (APPCAP) by the central government of China, a dramatic decrease in PM 2.5 concentrations from 2013 to 2017 occurred. Premature mortality caused by PM 2.5 dropped from 1,078,800 in 2014 to 962,900 in 2017. The PM 2.5 caused 17-year average mortality ranges from 3800 in Hainan Province to 124,800 in Henan Province. The health cost benefits gained by the reduction of PM 2.5 pollution amounted to US $193,800 in 2017 (compared to the costs due to PM 2.5 concentrations in 2013), amounting to 1.58% of the total national GDP. The impacts of urbanization on PM 2.5 concentration and mortality are analyzed. The PM 2.5 concentration and its induced mortality density in dense urban areas are much higher than those in rural areas. The aggravation of PM 2.5 associated premature mortality in urban areas is mainly due to the larger amount of emissions and to urban migration, and 6500 deaths in 2014 could have been avoided were the population ratios in dense-urban/normal-urban/rural areas to be reversed to the ones in 2001. It is recommended that people with respiratory-related diseases live in rural areas, where the pollutant concentration is relatively low., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The Pearl River Delta region (PRD) is the economic growth engine of China and also one of the most urbanized regions in the world. As a two-sided sword, rapid economic development causes air pollution and poses adverse health effects to the citizens in this area. This work estimated the negative health effects in the PRD caused by the four major ambient pollutants (SO2, NO2, O3 and PM10) from 2010 to 2013 by using a log linear exposure-response function and the WRF-CMAQ modeling system. Economic loss due to mortality and morbidity was evaluated by the value of statistical life (VSL) and cost of illness (COI) methods. The results show that the overall possible short-term all-cause mortality due to NO2, O3 and PM10 reached the highest in 2013 with the values being 13,217-22,800. The highest total economic loss, which ranged from 14,768 to 25,305million USD, occurred in 2013 and was equivalent to 1.4%-2.3% of the local gross domestic product. The monthly profile of cases of negative health effects varied by city and the types of ambient pollutants. The ratio of mortality attributed to air pollutants to total population was higher in urban areas than in rural areas. People living in the countryside should consider the possible adverse health effects of urban areas before they plan a move to the city. The results show that the health burden caused by the ambient pollutants over this region is serious and suggest that tighter control policies should be implemented in the future to reduce the level of air pollution., (Copyright © 2016 Elsevier B.V. All rights reserved.)

As one of the most notorious atmospheric pollutants, NOx not only promotes the formation of ozone but also has adverse health effects on humans. It is therefore of great importance to study the sources of NOx and its effects on human health. The Comprehensive Air Quality Model (CAMx) modeling system and ozone source apportionment technology (OSAT) were used to study the contribution of NOx from different emission sources over southern China. The results indicate that heavy duty diesel vehicles (HDDVs) and industrial point sources are the two major local NOx sources, accounting for 30.8% and 18.5% of local NOx sources, respectively. In Hong Kong, marine emissions contributed around 43.4% of local NOx in 2011. Regional transport is another important source of this pollutant, especially in February and November, and it can contribute over 30% of ambient NOx on average. Power plant point emission is an significant regional source in Zhuhai, Zhongshan and Foshan. The total emission sources are estimated to cause 2119 (0-4405) respiratory deaths and 991 (0-2281) lung cancer deaths due to long-term exposure to NOx in the Pearl River Delta region. Our results suggest that local governments should combine their efforts and vigorously promote further reduction of NOx emissions, especially for those sources that make a substantial contribution to NOx emissions and affect human health: HDDV, LDGV, industrial point sources and marine sources., (Copyright © 2016. Published by Elsevier Ltd.)

In this study, a high-resolution frontal area density (FAD) map that depicts the surface roughness of urban Hong Kong is produced using a mapping method that takes into account the dense urban morphology and the site wind availability of the territory. Using the MM5/CALMET model simulated wind data of Hong Kong, the FAD map of three urban zones are calculated: podium (0-15 m), building (15-60 m), and urban canopy (0-60 m). The wind tunnel test data is used to correlate the FAD understanding of the three zones. The grid sensitivity test indicates that 200 m × 200 m is the reasonable resolution for the FAD map; the test also establishes that the lower urban podium zone yields the best correlation with the experimental data. The study further establishes that the simpler two-dimensional ground coverage ratio (GCR), which is readily available in the planning circle, can be used to predict the area's average pedestrian level urban ventilation performance of the city. Working with their inhouse GIS team using available data, it allows the planners a way to understand the urban ventilation of the city for decisions related to air paths, urban permeability and site porosity., (Copyright © 2011 Elsevier B.V. All rights reserved.)