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This study examined the spatial and temporal variations of severe particulate pollution days (SPPDs) in China by using observed PM 2.5 concentrations during April 2013 to February 2018 from the Ministry of Environmental Protection of China. SPPDs were defined as those with observed daily mean PM 2.5 concentrations larger than 150 μg m−3. Observations showed that northern China had the highest number of SPPDs during the studied period. Since 2015, the number of SPPDs in northwestern China is comparable to or even higher than that observed in Beijing-Tianjin-Hebei (BTH). The highest numbers of SPPDs observed within BTH and the Yangtze River Delta (YRD) were 122 (33), 95 (17), 57 (15), 78 (18), and 31 (25) days in 2013, 2014, 2015, 2016, and 2017, respectively, indicating a general decreasing trend as a result of emission reduction measures. SPPDs occurred mainly from November to February in BTH and in December and January in the YRD. The major circulation patterns associated with large-scale SPPDs were analyzed by using principal component analysis. Five typical synoptic weather patterns were identified for BTH. The most dominant weather type (a cold high centered over the Xinjiang and Mongolian regions) for BTH was also responsible for most of the SPPDs in the YRD. These results have important implications for emission control strategies during SPPDs. Emission control measures can be applied once the dominant circulation patterns have been predicted. Image 1 • Observed severe particulate pollution days (SPPDs) in China are presented. • Interannual variations of SPPDs in BTH are influenced largely by meteorology. • Most dominant weather patterns for SPPDs in BTH and YRD are identified. Severe particulate pollution days(SPPDs) in China are quantified. Most dominant weather patterns for SPPDs in BTH and YRD are identified. [ABSTRACT FROM AUTHOR]

Aerosol optical properties were studied over Chisinau in Moldova, one of the longest running AERONET sites in Eastern Europe. During two decades (September 1999–November 2018), the mean aerosol optical depth (AOD) and Angstrom exponent (AE) were observed as 0.21 ± 0.13 and 1.49 ± 0.29, respectively. The highest AOD (0.24 ± 0.13) and AE (1.60 ± 0.26) were observed during the summer. More than half (∼55%) of the share was occupied by clean continental aerosols with seasonal order of winter (74.8%) > autumn (62%) > spring (48.9%) > summer (44.8%) followed by mixed aerosols with a respective contribution of 30.7% (summer), 28.4% (spring), 22.5 (autumn) and 16.4% (winter). A clear dominance of volume size distribution in the fine mode indicated the stronger influence of anthropogenic activities resulting in fine aerosol load in the atmosphere. The peak in the fine mode was centered at 0.15 μm, whereas that of the coarse mode was centered either at 3.86 μm (summer and autumn) or 5.06 μm (spring and winter). 'Extreme' aerosol events were observed during 21 days with a mean AOD (AE) of 0.99 ± 0.32 (1.43 ± 0.43), whereas 'strong' events were observed during 123 days with a mean AOD (AE) of 0.57 ± 0.07 (1.44 ± 0.40), mainly influenced by anthropogenic aerosols (during 19 and 101 days of each event type) from urban/industrial and biomass burning indicated by high AE and fine mode fraction. During the whole period (excluding events days), the fine and coarse mode peaks were observed at the radius of 0.15 and 5.06 μm, which in the case of extreme (strong) events were at 0.19 (0.15) and 3.86 (2.24) μm respectively. The fine mode volume concentration was 4.78 and 3.32 times higher, whereas the coarse mode volume concentration was higher by a factor of 1.98 and 2.27 during extreme and strong events compared to the whole period. [Display omitted] • Study over one of the longest running AERONET sites in Eastern Europe • AOD higher by a factor of 4.7 and 2.7 during events but with similar AE • Clean continental aerosols is dominant followed by mixed aerosols • Regional scale air mass transported the aerosols during events. [ABSTRACT FROM AUTHOR]

Air pollution in China has been very serious during the recent decades. However, few studies have investigated the effects of short-term exposure to PM 2.5 and O 3 on daily outpatient visits for respiratory diseases. We examined the effects of PM 2.5 and O 3 on the daily outpatient visits for respiratory diseases, explored the sensitivities of different population subgroups and analyzed the relative risk (RR) of PM 2.5 and O 3 in different seasons in Shanghai during 2013–2016. The generalized linear model (GLM) was applied to analyze the exposure-response relationship between air pollutants (daily average PM 2.5 and daily maximum 8-h average O 3 ), and daily outpatient visits due to respiratory diseases. The sensitivities of males and females at the ages of 15–60 yr-old and 60+ yr-old to the pollutants were also studied for the whole year and for the cold and warm months, respectively. Finally, the results of the single-day lagged model were compared with that of the moving average lag model. At lag 0 day, the RR of respiratory outpatients increased by 0.37% with a 10 μg/m 3 increase in PM 2.5 . Exposure to PM 2.5 (RR, 1.0047, 95% CI, 1.0032–1.0062) was more sensitive for females than for males (RR, 1.0025, 95% CI, 1.0008–1.0041), and was more sensitive for the 15-60 yr-old (RR, 1.0041, 95% CI, 1.0027–1.0055) than the 60+ yr-old age group (RR, 1.0031, 95% CI, 1.0014–1.0049). O 3 was not significantly associated with respiratory outpatient visits during the warm periods, but was negatively associated during the cold periods. PM 2.5 was more significantly in the cold periods than that in the warm periods. The results indicated that control of PM 2.5 , compared to O 3 , in the cold periods would be more beneficial to the respiratory health in Shanghai. In addition, the single-day lagged model underestimated the relationship between PM 2.5 and O 3 and outpatient visits for respiratory diseases compared to the moving average lag model. [ABSTRACT FROM AUTHOR]

A Community Multiscale Air Quality (CMAQ) model with source-oriented lumped SAPRC-11 (S11L) photochemical mechanism and secondary organic aerosol (SOA) module was applied to determine the contributions of anthropogenic and biogenic sources to SOA concentrations in China. A one-year simulation of 2013 using the Multi-resolution Emission Inventory for China (MEIC) shows that summer SOA are generally higher (10–15 μg m −3 ) due to large contributions of biogenic (country average 60%) and industrial sources (17%). In winter, SOA formation was mostly due to anthropogenic emissions from industries (40%) and residential sources (38%). Emissions from other countries in southeast China account for approximately 14% of the SOA in both summer and winter, and 46% in spring due to elevated open biomass burning in southeast Asia. The Regional Emission inventory in ASia v2.1 (REAS2) was applied in this study for January and August 2013. Two sets of simulations with the REAS2 inventory were conducted using two different methods to speciate total non-methane carbon into model species. One approach uses total non-methane hydrocarbon (NMHC) emissions and representative speciation profiles from the SPECIATE database. The other approach retains the REAS2 speciated species that can be directly mapped to S11L model species and uses source specific splitting factors to map other REAS2 lumped NMHC species. Biogenic emissions are still the most significant contributor in summer based on these two sets of simulations. However, contributions from the transportation sector to SOA in January are predicted to be much more important based on the two REAS2 emission inventories (∼30–40% vs. ∼5% by MEIC), and contributions from residential sources according to REAS2 was much lower (∼21–24% vs. ∼42%). These discrepancies in source contributions to SOA need to be further investigated as the country seeks for optimal emission control strategies to fight severe air pollution. [ABSTRACT FROM AUTHOR]

In recent years, severe pollution events were observed frequently in India especially at its capital, New Delhi. However, limited studies have been conducted to understand the sources to high pollutant concentrations for designing effective control strategies. In this work, source-oriented versions of the Community Multi-scale Air Quality (CMAQ) model with Emissions Database for Global Atmospheric Research (EDGAR) were applied to quantify the contributions of eight source types (energy, industry, residential, on-road, off-road, agriculture, open burning and dust) to fine particulate matter (PM 2.5 ) and its components including primary PM (PPM) and secondary inorganic aerosol (SIA) i.e. sulfate, nitrate and ammonium ions, in Delhi and three surrounding cities, Chandigarh, Lucknow and Jaipur in 2015. PPM mass is dominated by industry and residential activities (>60%). Energy (∼39%) and industry (∼45%) sectors contribute significantly to PPM at south of Delhi, which reach a maximum of 200 μg/m 3 during winter. Unlike PPM, SIA concentrations from different sources are more heterogeneous. High SIA concentrations (∼25 μg/m 3 ) at south Delhi and central Uttar Pradesh were mainly attributed to energy, industry and residential sectors. Agriculture is more important for SIA than PPM and contributions of on-road and open burning to SIA are also higher than to PPM. Residential sector contributes highest to total PM 2.5 (∼80 μg/m 3 ), followed by industry (∼70 μg/m 3 ) in North India. Energy and agriculture contribute ∼25 μg/m 3 and ∼16 μg/m 3 to total PM 2.5 , while SOA contributes <5 μg/m 3 . In Delhi, industry and residential activities contribute to 80% of total PM 2.5 . [ABSTRACT FROM AUTHOR]

A high-resolution inventory of primary atmospheric pollutants from coal-fired power plants in Shaanxi in 2012 was built based on a detailed database compiled at unit level involving unit capacity, boiler size and type, commission time, corresponding control technologies, and average coal quality of 72 power plants. The pollutants included SO 2 , NO x , fine particulate matter (PM 2.5 ), inhalable particulate matter (PM 10 ), organic carbon (OC), elemental carbon (EC), carbon monoxide (CO) and non-methane volatile organic compounds (NMVOC). Emission factors for SO 2 , NO x , PM 2.5 and PM 10 were adopted from standardized official promulgation, supplemented by those from local studies. The estimated annual emissions of SO 2 , NO x , PM 2.5 , PM 10 , EC, OC, CO and NMVOC were 152.4, 314.8, 16.6, 26.4, 0.07, 0.27, 64.9 and 2.5 kt, respectively. Small units (< 100 MW), which accounted for ~ 60% of total unit numbers, had less coal consumption but higher emission rates compared to medium (≥ 100 MW and < 300 MW) and large units (≥ 300 MW). Main factors affecting SO 2 , NO x , PM 2.5 and PM 10 emissions were decontamination efficiency, sulfur content and ash content of coal. Weinan and Xianyang were the two cities with the highest emissions, and Guanzhong Plain had the largest emission density. Despite the projected growth of coal consumption, emissions would decrease in 2030 due to improvement in emission control technologies and combustion efficiencies. SO 2 and NO x emissions would experience significant reduction by ~ 81% and ~ 84%, respectively. PM 2.5 , PM 10 , EC and OC would be decreased by ~ 43% and CO and NMVOC would be reduced by ~ 16%. [ABSTRACT FROM AUTHOR]

Due to inherent errors in the chemical transport models, inaccuracies in the input data, and simplified chemical mechanisms, ozone (O 3) predictions are often biased from observations. Accurate O 3 predictions can better help assess its impacts on public health and facilitate the development of effective prevention and control measures. In this study, we used a random forest (RF) model to construct a bias-correction model to correct the bias in the predictions of hourly O 3 (O 3 -1h), daily maximum 8-h O 3 (O 3 -Max8h), and daily maximum 1-h O 3 (O 3 -Max1h) concentrations from the Community Multi-Scale Air Quality (CMAQ) model in the Yangtze River Delta region. The results show that the RF model successfully captures the nonlinear response relationship between O 3 and its influence factors, and has an outstanding performance in correcting the bias of O 3 predictions. The normalized mean biases (NMBs) of O 3 -1h, O 3 -Max8h, and O 3 -Max1h decrease from 15.8%, 20.0%, and 17.0.% to 0.5%, −0.8%, and 0.1%, respectively; correlation coefficients increase from 0.78, 0.90, and 0.89 to 0.94, 0.95, and 0.94, respectively. For O 3 -1h and O 3 -Max8h, the original CMAQ model shows an obvious bias in the central and southern Zhejiang region, while the RF model decreases the NMB values from 54% to −1% and 34% to −4%, respectively. The O 3 -1h bias is mainly caused by the bias of nitrogen dioxide (NO 2). Relative humidity and temperature are also important factors that lead to the bias of O 3. For high O 3 concentrations, the temperature bias and O 3 observations are the major reasons for the discrepancy between the model and the observations. [Display omitted] • The RF model greatly improves accuracy of hourly O 3 predictions from CMAQ. • The RF model significantly reduced O 3 bias in the central and southern Zhejiang. • O3-1h bias is mainly caused by the bias of nitrogen dioxide. • Relative humidity and temperature have important impacts on the O 3 bias. [ABSTRACT FROM AUTHOR]

Ground-based observational characterization of atmosphere aerosols over Central Asia is very limited. This study investigated the columnar aerosol characteristics over Issyk-Kul, Kyrgyzstan, a background site in Central Asia using the long-term (∼14 years: August 2007–November 2021) data acquired with the Cimel sunphotometer. The mean aerosol optical depth (AOD) and Ångström exponent (AE) during the observation period were 0.14 ± 0.10 and 1.19 ± 0.41, respectively. Both AOD and AE varied across seasons, with highest AOD in spring (0.17 ± 0.17). Regarding the aerosol types, clean continental aerosols were dominant type (65%), followed by mixed aerosols (∼19%), clean marine aerosols (∼14%), dust (0.8%), and urban/industrial and biomass burning aerosol (0.7%). The aerosol volume size distribution was bimodal indicating the influence of both anthropogenic and natural aerosols with clear dominance of coarse mode during the spring season. Mainly dust and mixed aerosols were present during high aerosol episodes while the coarse mode aerosol volume concentration was 7.5 (strong episodes) and ∼19 (extreme episodes) times higher than the whole period average. Aerosol over this background sites were from local and regional sources with some contribution of long-range transport. [Display omitted] • Study on modern day aerosol load over a background site in Central Asia. • Clean continental aerosol was dominant followed by mixed aerosol. • Coarse aerosols were responsible for occurrence of heavy aerosol events. • Local and regional emissions were dominant aerosol sources. [ABSTRACT FROM AUTHOR]

Introduction Intrauterine growth restriction has been associated with exposure to air pollution, but there is a need to clarify which sources and components are most likely responsible. This study investigated the associations between low birth weight (LBW, < 2500 g) in term born infants (≥ 37 gestational weeks) and air pollution by source and composition in California, over the period 2001–2008. Methods Complementary exposure models were used: an empirical Bayesian kriging model for the interpolation of ambient pollutant measurements, a source-oriented chemical transport model (using California emission inventories) that estimated fine and ultrafine particulate matter (PM 2.5 and PM 0.1 , respectively) mass concentrations (4 km × 4 km) by source and composition, a line-source roadway dispersion model at fine resolution, and traffic index estimates. Birth weight was obtained from California birth certificate records. A case-cohort design was used. Five controls per term LBW case were randomly selected (without covariate matching or stratification) from among term births. The resulting datasets were analyzed by logistic regression with a random effect by hospital, using generalized additive mixed models adjusted for race/ethnicity, education, maternal age and household income. Results In total 72,632 singleton term LBW cases were included. Term LBW was positively and significantly associated with interpolated measurements of ozone but not total fine PM or nitrogen dioxide. No significant association was observed between term LBW and primary PM from all sources grouped together. A positive significant association was observed for secondary organic aerosols. Exposure to elemental carbon (EC), nitrates and ammonium were also positively and significantly associated with term LBW, but only for exposure during the third trimester of pregnancy. Significant positive associations were observed between term LBW risk and primary PM emitted by on-road gasoline and diesel or by commercial meat cooking sources. Primary PM from wood burning was inversely associated with term LBW. Significant positive associations were also observed between term LBW and ultrafine particle numbers modeled with the line-source roadway dispersion model, traffic density and proximity to roadways. Discussion This large study based on complementary exposure metrics suggests that not only primary pollution sources (traffic and commercial meat cooking) but also EC and secondary pollutants are risk factors for term LBW. [ABSTRACT FROM AUTHOR]

A source-oriented CMAQ was applied to determine source sector/region contributions to primary particulate matter (PPM) in China. Four months were simulated with emissions grouped to eight regions and six sectors. Predicted elemental carbon (EC), primary organic carbon (POC), and PPM concentrations and source contributions agree with measurements and have significant spatiotemporal variations. Residential is a major contributor to spring/winter EC (50–80%), POC (60%–90%), and PPM (30–70%). For summer/fall, industrial contributes 30–50% for EC/POC and 40–60% for PPM. Transportation is more important for EC (20–30%) than POC/PPM (<5%). Open burning is important in summer/fall of Guangzhou and Chongqing. Dust contributes to 1/3–1/2 in spring/fall of Beijing, Xi'an and Chongqing. Based on sector–region combination, local residential/transportation and residential/industrial from Heibei are major contributors to spring PPM in Beijing. In summer/fall, local industrial is the largest. In winter, residential/industrial from local and Hebei account for >90% in Beijing. [ABSTRACT FROM AUTHOR]

Projecting future air pollution and related health burdens remains challenging because of the complex interactions among future emissions, population, and climate change. In this study, we estimated the premature deaths attributed to ambient fine particulate matter (PM 2.5) and ozone (O 3) from 2015 to 2100 under four socioeconomic climate scenarios based on an age-stratified assessment method. We found that PM 2.5 will decrease in all shared socioeconomic pathway (SSP) scenarios and O 3 will decrease in the SSP1-2.6 and SSP2-4.5 scenarios, contributing to a decrease in premature mortality together with the declining total population in China. However, the benefits of a decline in population size and PM 2.5 and O 3 concentrations over time will be largely offset by population aging, and premature death caused by PM 2.5 and O 3 will continue to rise till 2060–2080. This impact was greater for the O 3 -related deaths than those for PM 2.5. Our study highlights the importance of future prevention strategies that must jointly improve air quality and susceptibility to aging. [ABSTRACT FROM AUTHOR]

Multi-pollutant air pollution (i.e., several pollutants reaching very high concentrations simultaneously) frequently occurs in many regions across China. Air quality index (AQI) is used worldwide to inform the public about levels of air pollution and associated health risks. The current AQI approach used in China is based on the maximum value of individual pollutants, and does not consider the combined health effects of exposure to multiple pollutants. In this study, two novel alternative indices – aggregate air quality index (AAQI) and health-risk based air quality index (HAQI) – were calculated based on data collected in six megacities of China (Beijing, Shanghai, Guangzhou, Shjiazhuang, Xi'an, and Wuhan) during 2013 to 2014. Both AAQI and HAQI take into account the combined health effects of various pollutants, and the HAQI considers the exposure (or concentration)-response relationships of pollutants. AAQI and HAQI were compared to AQI to examine the effectiveness of the current AQI in characterizing multi-pollutant air pollution in China. The AAQI and HAQI values are higher than the AQI on days when two or more pollutants simultaneously exceed the Chinese Ambient Air Quality Standards (CAAQS) 24-hour Grade II standards. The results of the comparison of the classification of risk categories based on the three indices indicate that the current AQI approach underestimates the severity of health risk associated with exposure to multi-pollutant air pollution. For the AQI-based risk category of ‘unhealthy’, 96% and 80% of the days would be ‘very unhealthy’ or ‘hazardous’ if based on AAQI and HAQI, respectively; and for the AQI-based risk category of ‘very unhealthy’, 67% and 75% of the days would be ‘hazardous’ if based on AAQI and HAQI, respectively. The results suggest that the general public, especially sensitive population groups such as children and the elderly, should take more stringent actions than those currently suggested based on the AQI approach during high air pollution events. Sensitivity studies were conducted to examine the assumptions used in the AAQI and HAQI approaches. Results show that AAQI is sensitive to the choice of pollutant irrelevant constant. HAQI is sensitive to the choice of both threshold values and pollutants included in total risk calculation. [ABSTRACT FROM AUTHOR]

The Community Multi-Scale Air Quality (CMAQ) model was applied to evaluate the air quality in the coastal city of Kannur, India, during the 2020 COVID-19 lockdown. From the Pre1 (March 1–24, 2020) period to the Lock (March 25–April 19, 2020) and Tri (April 20–May 9, 2020) periods, the Kerala state government gradually imposed a strict lockdown policy. Both the simulations and observations showed a decline in the PM 2.5 concentrations and an enhancement in the O 3 concentrations during the Lock and Tri periods compared with that in the Pre1 period. Integrated process rate (IPR) analysis was employed to isolate the contributions of the individual atmospheric processes. The results revealed that the vertical transport from the upper layers dominated the surface O 3 formation, comprising 89.4%, 83.1%, and 88.9% of the O 3 sources during the Pre1, Lock, and Tri periods, respectively. Photochemistry contributed negatively to the O 3 concentrations at the surface layer. Compared with the Pre1 period, the O 3 enhancement during the Lock period was primarily attributable to the lower negative contribution of photochemistry and the lower O 3 removal rate by horizontal transport. During the Tri period, a slower consumption of O 3 by gas-phase chemistry and a stronger vertical import from the upper layers to the surface accounted for the increase in O 3. Emission and aerosol processes constituted the major positive contributions to the net surface PM 2.5 , accounting for a total of 48.7%, 38.4%, and 42.5% of PM 2.5 sources during the Pre1, Lock, and Tri periods, respectively. The decreases in the PM 2.5 concentrations during the Lock and Tri periods were primarily explained by the weaker PM 2.5 production from emission and aerosol processes. The increased vertical transport rate of PM 2.5 from the surface layer to the upper layers was also a reason for the decrease in the PM 2.5 during the Lock periods. [Display omitted] • Photochemistry contributes negatively on O 3 concentrations at the surface layer. • Lower chemical consumption and stronger vertical import lead to O 3 increase. • Emission and aerosol processes are the major contributors to surface PM 2.5 • Weaker emission and aerosol processes explain PM 2.5 decrease. [ABSTRACT FROM AUTHOR]

Particle number concentration (PNC) is an important parameter for evaluating the environmental health and climate effects of particulate matter (PM). A good understanding of PNC is essential to control atmospheric ultrafine particles (UFP) and protect public health. In this study, we reviewed the PNC studies in the literature aimed to gain a comprehensive understanding about the levels, trends, and sources of PNC in China. The PNC levels at the urban, suburban, rural, remote, and coastal sites in China were 8500–52,200, 8600–30,300, 8600–28,400, 2100–16,100, and 5700-19,600 cm−3, respectively. The wide ranges of PNC indicate significant heterogeneity in the spatial distribution of PNC, but also are partly due to the different measurement techniques deployed in different studies. In general, it still can be concluded that the PNC levels at urban > suburban > rural > coastal > remote sites. Except for Mt. Waliguan (a remote site of 3816 m a.s.l.), other cities had the highest PNC in spring or winter and the lowest in summer or autumn. Long-term changes of PNCs in Beijing and Nanjing indicated that PNCs of Nucleation and Aitken modes had substantially declined following stricter emission controls in recent years, but more frequent new particle formation (NPF) events were observed due to reduction in coagulation sink. Overall, traffic emission was the most dominant source of PNC in more than 94.4% of the selected cities around the world, while combustion2 (the energy production and industry related combustion source), background aerosol, and nucleation sources were also important contributors to PNC. This study provides insights about PNC and its sources around the world, especially in China. A few recommendations were suggested to further improve the understanding of PNC and to develop effective PNC control strategies. [Display omitted] • The levels, trends, and sources of PNC in China were obtained from 109 PNC studies. • PNCs at the urban sites in China are higher than rural, coastal and remote sites. • PNCs of Nucleation and Aitken modes have substantially declined during recent years. • The top three sources of PNCs are traffic, cooking and biomass burning in Beijing. [ABSTRACT FROM AUTHOR]

• 178 nitrate modeling studies in China published during 2007–2021 were reviewed. • Model performance on nitrate and possible reasons for model-observation biases were summarized. • The contribution of N 2 O 5 heterogeneous chemistry to nitrate varies from 21.0% to 51.6% among studies. • Decreased SO 2 emission, enhanced AOC, and weakened deposition account for increasing PM 2.5 nitrate fraction. • Multiple-pollutant control strategies involving NH 3 , NO x , and VOCs are needed to mitigate nitrate pollution. Particulate nitrate (pNO 3) is now becoming the principal component of PM 2.5 during severe winter haze episodes in many cities of China. To gain a comprehensive understanding of the key factors controlling pNO 3 formation and driving its trends, we reviewed the recent pNO 3 modeling studies which mainly focused on the formation mechanism and recent trends of pNO 3 as well as its responses to emission controls in China. The results indicate that although recent chemical transport models (CTMs) can reasonably capture the spatial–temporal variations of pNO 3 , model-observation biases still exist due to large uncertainties in the parameterization of dinitrogen pentoxide (N 2 O 5) uptake and ammonia (NH 3) emissions, insufficient heterogeneous reaction mechanism, and the predicted low sulfate concentrations in current CTMs. The heterogeneous hydrolysis of N 2 O 5 dominates nocturnal pNO 3 formation, however, the contribution to total pNO 3 varies among studies, ranging from 21.0% to 51.6%. Moreover, the continuously increasing PM 2.5 pNO 3 fraction in recent years is mainly due to the decreased sulfur dioxide emissions, the enhanced atmospheric oxidation capacity (AOC), and the weakened nitrate deposition. Reducing NH 3 emissions is found to be the most effective control strategy for mitigating pNO 3 pollution in China. This review suggests that more field measurements are needed to constrain the parameterization of heterogeneous N 2 O 5 and nitrogen dioxide (NO 2) uptake. Future studies are also needed to quantify the relationships of pNO 3 to AOC, O 3 , NOx, and volatile organic compounds (VOCs) in different regions of China under different meteorological conditions. Research on multiple-pollutant control strategies involving NH 3 , NO X, and VOCs is required to mitigate pNO 3 pollution, especially during severe winter haze events. [ABSTRACT FROM AUTHOR]

Average PM 2.5 concentrations of ∼250 μg m −3 and peak concentrations of ∼500 μg m −3 were observed in Xi'an, the largest city in Northwest China during an extreme event in January 2013. The source-oriented versions of the Community Multi-scale Air Quality (CMAQ) model with anthropogenic emissions from Emissions Database for Global Atmospheric Research (EDGAR) were used to study the source contributions of six different source categories including energy production, industries, transportation, residential activities, “other” (agriculture, biomass, waste burning, and biogenic sources), and windblown dust to primary and secondary inorganic PM 2.5 (nitrate and sulfate) during this episode. The model generally captured the variation and magnitude of PM 2.5 concentrations at monitoring sites. The monthly average concentration of the predicted PM 2.5 in Xi'an was >200 μg m −3 , comparing favorably with the measurement of ∼250 μg m −3 . Predicted concentrations of elemental carbon (EC), organic aerosol (OA), sulfate, nitrate, and ammonium were 6, 35, 18, 22, and 12 μg m −3 , respectively. Chemically unresolved PM 2.5 components (PM 2.5 Other) were ∼80 μg m −3 . Industries and residential activities dominated EC, organic carbon (OC) and PM 2.5 Other, contributing 85%, 95%, and 83%, respectively. Energy production (mainly coal combustion) was the dominating source for secondary nitrate, contributing 46%. Other local and upwind sources were also important, contributing 43% and 11% of total nitrate, respectively. Primary sulfate was ∼10 μg m −3 in vicinity surrounding point sources. Secondary sulfate from upwind sources was also important with concentrations of ∼4–5 μg m −3 . Secondary sulfate formed by SO 2 emitted from local sources was dominated by energy production. Based on the contributions of different sources to primary components and secondary nitrate and sulfate, the contributions of different sources to PM 2.5 total mass in Xi'an during the extremely polluted months are: energy 5%, industries 58%, transportation 2%, residential activities 16%, dust 4%, and other (including other components, inexplicit sources, and upwind sources) 15%. [ABSTRACT FROM AUTHOR]

Reducing population exposure to PM 2.5 in the eastern US will require control of secondary sulfate and nitrate. A source-oriented Community Multi-scale Air Quality (CMAQ) model is used to determine contributions of major emission sources to nitrate and sulfate concentrations in the seven eastern US cities (New York City, Pittsburgh, Baltimore, Chicago, Detroit, St. Paul, and Winston-Salem) in January and August of 2000 and 2006. Identified major nitrate sources include on-road gasoline-powered vehicles, diesel engines, natural gas and coal combustion. From 2000 to 2006, January nitrate concentrations decreased by 25–68% for all the seven cities. On average, ~ 53% of this change was caused by emissions controls while 47% was caused by meteorology variations. August nitrate concentrations decreased by a maximum of 68% in New York City but Detroit experienced increasing August nitrate concentrations by up to 33%. On average, ~ 33% of the reduction in nitrate is offset by increases associated with meteorological conditions that favor nitrate formation. Coal combustion and natural gas are the dominant sources for sulfate in both seasons. January sulfate decrease from 2000 to 2006 in all cities by 4–58% except New York City, which increases by 13%. On average, ~ 93% of the reduction in sulfate was attributed to emission controls with 7% associated with changes in meteorology. August sulfate concentrations decrease by 11–44% in all cities. On average, emission controls alone between 2000 and 2006 would have caused 6% more reduction but the effectiveness of the controls was mitigated by meteorology conditions more favorable to sulfate production in 2006 vs. 2000. The results of this study suggest that regional emissions controls between 2000 and 2006 have been effective at reducing population exposure to PM 2.5 in the eastern US, but yearly variations in meteorology must be carefully considered when assessing the exact magnitude of the control benefits. [ABSTRACT FROM AUTHOR]

Highlights: [•] Synthesized a simple probe which responds sensitively toward pH change both in vitro and in vivo. [•] This probe emits bright red light in acid solution and green light in alkaline solution, and the color change can be converted in multiple cycles along with the dynamic change of pH. [•] Compared with other pH probes reported, this probe has good water solubility, negligible cytotoxicity, and can permeate cell membrane and nucleus in which the pH value is closely related to many diseases. [•] Probe 2 can also be used as a lysosome maker in living cells. These findings suggest that this probe has potential application in pH detecting for disease diagnosis. [ABSTRACT FROM AUTHOR]

Abstract: Detection of DNA damage has been greatly improved following the development of equipment and techniques, however, discrimination between 5-hydroxymethylcytosine (5-hmC) and 5-methylcytosine (5-mC) is still a thorny problem. In the present study, an approach to oxidize and selective label (Ox-Labeling) 5-hmC in native DNA has been reported, which conveniently distinguishes 5-hmC from 5-mC using simple and effective processes. [Copyright &y& Elsevier]

Abstract: Comparisons were made between three sets of meteorological fields used to support air quality predictions for the California Regional Particulate Air Quality Study (CRPAQS) winter episode from December 15, 2000 to January 6, 2001. The first set of fields was interpolated from observations using an objective analysis method. The second set of fields was generated using the WRF prognostic model without data assimilation. The third set of fields was generated using the WRF prognostic model with the four-dimensional data assimilation (FDDA) technique. The UCD/CIT air quality model was applied with each set of meteorological fields to predict the concentrations of airborne particulate matter and gaseous species in central California. The results show that the WRF model without data assimilation over-predicts surface wind speed by ∼30% on average and consequently yields under-predictions for all PM and gaseous species except sulfate (S(VI)) and ozone(O3). The WRF model with FDDA improves the agreement between predicted and observed wind and temperature values and consequently yields improved predictions for all PM and gaseous species. Overall, diagnostic meteorological fields produced more accurate air quality predictions than either version of the WRF prognostic fields during this episode. Population-weighted average PM2.5 exposure is 40% higher using diagnostic meteorological fields compared to prognostic meteorological fields created without data assimilation. These results suggest diagnostic meteorological fields based on a dense measurement network are the preferred choice for air quality model studies during stagnant periods in locations with complex topography. [Copyright &y& Elsevier]

Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO 2). Current studies in China at the national scale were less focused on NO 2 exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO 2 predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO 2 , TROPOspheric Monitoring Instrument NO 2 , and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO 2 concentrations from 2013 to 2019 across China at 1×1 km2 resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R2 = 0.72) and the spatial (R2 = 0.85) variations of the NO 2 predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R2 > 0.68) or regions far away from monitors (CV R2 > 0.63). We identified a clear decreasing trend of NO 2 exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%–14% in some megacities and captured substantial NO 2 variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term). [Display omitted] • A national ensemble model was developed for NO 2 incorporating multisource predictors. • Daily NO 2 exposures at 1 × 1 km resolution were estimated in China nationwide from 2013 to 2019. • Better performance in megacities for NO 2 exposure downscaled to 100 m resolution. • The study model is useful for epidemiological studies with various study domain and settings. [ABSTRACT FROM AUTHOR]