Search

The unique energy structure, high intensity of coal production, and complex terrain, make Fenwei Plain a highly polluted region in China. In this study, we characterized the transport characteristic and sources of PM2.5 (the fraction of particulate matter ≤ 2.5 μm) in Sanmenxia, a polluted city in canyon terrain. The results showed that special topography in Sanmenxia had an important role in the transport of particulates. Sanmenxia is located between two northeast-southwest facing mountains, showing a special local circulation. The local circulation was dominated by a downslope wind at nighttime, while the cross−mountain airflow and zonal wind were dominant during the daytime in the canyon terrain. PM2.5 accumulated near Sanmenxia with the influence of downslope, zonal wind, and topography. The main regional transport paths could be summarized into an eastern path, a northern path, and a western path during the severe haze episodes. The PM2.5 source apportionment revealed by an on-line tracer-tagged of the Nested Air Quality Prediction Model System (NAQPMS) showed that the main regional sources of Sanmenxia were Yuncheng, Sanmenxia, and Weinan. The contribution to PM2.5 concentration in Sanmenxia was 39%, 25%, and 11%, respectively. The northern path had the most important impact on Sanmenxia. The results can provide scientific basis for the establishment of severe haze control in Sanmenxia and regional joint control. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM) is object of particular attention due to its health effects. It is currently regulated by adopting PM2.5 as an indicator to control anthropogenic combustion emissions. Therefore, it is crucial to collect aerosol samples representative of such sources, without including PM from natural sources. Thus, a clean separation between coarse and fine mode aerosol should be set. With this purpose, aerosol size mass distribution was taken in the aerodynamic diameter range from 0.5 to 10 µm. In comparison with a base scenario, characterized by local pollution sources, three case studies were considered, involving desert dust advection, sea salt advection and forest fire aerosol from a remote area. In the base scenario, PM2.5 represented a suitable fine-mode indicator, whereas it was considerably affected by coarse PM in case of desert dust and sea salt aerosol advection. Such interference was considerably reduced by setting the fine/coarse separation at 1.0 µm. Such separation underrepresented fine PM from forest fire long-range transport, nonetheless in the case studies considered, PM1 represented the best indicator of fine aerosol since less affected by coarse natural sources. The data presented clearly support the results from other studies associating the health effects of PM2.5 to PM1, rather than to PM1–2.5. Overall, there is a need to reconsider PM2.5 as an indicator of fine atmospheric aerosol. [ABSTRACT FROM AUTHOR]

Emissions and meteorology are significant factors affecting aerosol pollution, but it is not sufficient to understand their relative contributions to aerosol pollution changes. In this study, the observational data and the chemical model (GRAPES_CUACE) are combined to estimate the drivers of PM2.5 changes in various regions (the Beijing–Tianjin–Hebei (BTH), the Central China (CC), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)) between the first month after COVID-19 (FMC_2020) (i.e., from 23 January to 23 February 2020) and the corresponding period in 2019 (FMC_2019). The results show that PM2.5 mass concentration increased by 26% (from 61 to 77 µg m−3) in the BTH, while it decreased by 26% (from 94 to 70 µg m−3) in the CC, 29% (from 52 to 37 µg m−3) in the YRD, and 32% (from 34 to 23 µg m−3) in the PRD in FMC_2020 comparing with FMC_2019, respectively. In the BTH, although emissions reductions partly improved PM2.5 pollution (−5%, i.e., PM2.5 mass concentration decreased by 5% due to emissions) in FMC_2020 compared with that of FMC_2019, the total increase in PM2.5 mass concentration was dominated by more unfavorable meteorological conditions (+31%, i.e., PM2.5 mass concentration increased by 31% due to meteorology). In the CC and the YRD, emissions reductions (−33 and −36%) played a dominating role in the total decrease in PM2.5 in FMC_2020, while the changed meteorological conditions partly worsened PM2.5 pollution (+7 and +7%). In the PRD, emissions reductions (−23%) and more favorable meteorological conditions (−9%) led to a total decrease in PM2.5 mass concentration. This study reminds us that the uncertainties of relative contributions of meteorological conditions and emissions on PM2.5 changes in various regions are large, which is conducive to policymaking scientifically in China. [ABSTRACT FROM AUTHOR]

Visibility degradation is a pervasive environmental problem in winter in China and its prediction accuracy is therefore important, especially in low visibility conditions. However, current visibility parameterization algorithms tend to overestimate low visibility (<5 km) during haze–fog events. The key point of low visibility calculation and prediction depends on a reasonable understanding of the correlation between visibility, PM2.5 concentration, and relative humidity (RH). Using the observations of PM2.5 concentration and meteorology from December 2016 to February 2017, under different RH levels, the relative contribution differences of PM2.5 concentrations and RH to visibility degradation are investigated in depth. On this basis, new multiple nonlinear regressions for low visibility are developed for eight regions of China. The results show that under relatively low RH conditions (<80% or 85%), PM2.5 concentration plays a leading role in visibility changes in China. With the increase in RH (80–90% or 85–95%), the PM2.5 concentration corresponding to the visibility of 10 and 5 km decreases and the contribution of RH becomes increasingly important. When the RH grows to >95%, a relatively low PM2.5 concentration could also lead to visibility decreasing to <5 km. Within this range, the PM2.5 concentration corresponding to the visibility of 5 km in Central China (CC), Sichuan Basin (SCB), and Yangtze River Delta (YRD) is approximately 50, 50, and 30 μg m−3, and that in Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZP) is approximately 125 μg m−3, respectively. Specifically, based on these contribution differences, new multiple nonlinear regression equations of visibility, PM2.5 concentration, temperature, and dew point temperature of the eight regions (Scheme A) are established respectively after grouping the datasets by setting different RH levels (BTH, GZP, and North Eastern China (NEC): RH < 80%, 80 ≤ RH < 90% and RH ≥ 90%; CC, SCB, YRD, and South China Coastal (SCC): RH < 85%, 85 ≤ RH < 95% and RH ≥ 95%; Xinjiang (XJ): RH < 90% and RH ≥ 90%). According to the previous regression methods, we directly established the multiple regression models between visibility and the same factors as a comparison (Scheme B). Statistical results show that the advantage of Scheme A for 5 and 3 km evaluation is more significant compared with Scheme B. For the five low visibility regions (BTH, GZP, CC, SCB, and YRD), RMSEs of Scheme A under visibility <5 and 3 km are 0.77–1.01 and 0.48–0.95 km, 16–43 and 24–57% lower than those of Scheme B, respectively. Moreover, Scheme A reproduced the winter visibility in BTH, GZP, CC, SCB, YRD, and SCC from 2016 to 2020 well. The MAEs, MBs, and RMSEs under visibility < 5 km are 0.44–1.41, −1.33–1.24, and 0.58–2.36 km, respectively. Overall, Scheme A is confirmed to be reliable and applicable for low visibility prediction in many regions of China. This study provides a new visibility parameterization algorithm for the haze–fog numerical prediction system. [ABSTRACT FROM AUTHOR]

Based on measurement data from air quality monitoring stations, the spatio-temporal characteristics of the concentrations of particles with aerodynamic equivalent diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively) in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2018 were analysed at yearly, seasonal, monthly, daily and hourly scales. The results indicated that (1) from 2015 to 2018, the annual average values of PM2.5 and PM10 concentrations and the PM2.5/PM10 ratio in the study area decreased each year; (2) the particulate matter (PM) concentration in winter was significantly higher than that in summer, and the PM2.5/PM10 ratio was highest in winter and lowest in spring; (3) the PM2.5 and PM10 concentrations exhibited a pattern of double peaks and valleys throughout the day, reaching peak values at night and in the morning and valleys in the morning and afternoon; and (4) with the use of an improved sine function to simulate the change trend of the monthly mean PM concentration, the fitting R2 values for PM2.5 and PM10 in the whole study area were 0.74 and 0.58, respectively. Moreover, the high-value duration was shorter, the low-value duration was longer, and the concentration decrease rate was slower than the increase rate. [ABSTRACT FROM AUTHOR]

Air pollution, especially PM2.5 pollution, still seriously endangers the health of urban residents in China. The built environment is an important factor affecting PM2.5; however, the key factors remain unclear. Based on 37 neighborhoods located in five Chinese megacities, three relative indicators (the range, duration, and rate of change in PM2.5 concentration) at four pollution levels were calculated as dependent variables to exclude the background levels of PM2.5 in different cities. Nineteen built environment factors extracted from green space and gray space and three meteorological factors were used as independent variables. Principal component analysis was adopted to reveal the relationship between built environment factors, meteorological factors, and PM2.5. Accordingly, 24 models were built using 32 training neighborhood samples. The results showed that the adj_R2 of most models was between 0.6 and 0.8, and the highest adj_R2 was 0.813. Four principal factors were the most important factors that significantly affected the growth and reduction of PM2.5, reflecting the differences in green and gray spaces, building height and its differences, relative humidity, openness, and other characteristics of the neighborhood. Furthermore, the relative error was used to test the error of the predicted values of five verification neighborhood samples, finding that these models had a high fitting degree and can better predict the growth and reduction of PM2.5 based on these built environment factors. [ABSTRACT FROM AUTHOR]

In this study, a time change analysis of fine particulate (PM2.5) emission in multi-resolution emission inventory in China (MEIC) from 2013 to 2016 was conducted. It was found that PM2.5 emissions showed a decreasing trend year by year, and that the annual total emission of PM2.5 decreased by 28.5% in 2016 compared with that of 2013. When comparing the observation data of PM2.5 and ozone (O3), it was found that both PM2.5 and O3 show obvious seasonal changes. The emission of PM2.5 in autumn and winter is higher than that in summer, while that of O3 is not. Our study showed that in the 2015–2020 period, annual mean concentrations of PM2.5 and O3 in Beijing varied from 80.87 to 38.31 μg m−3 and 110.75 to 106.18 μg m−3, respectively. Since 2015, the observed value of PM2.5 has shown an obvious downward trend. Compared with 2015, the average annual PM2.5 concentrations in Beijing, Shanghai, Xuzhou, Zhengzhou, and Hefei in 2020 had decreased by 52.62%, 40.35%, 22.2%, 46.84%, and 45.11%, respectively, while O3 showed an upward trend. Compared with the annual averages of 2015 and 2020, Beijing and Shanghai saw a decrease of 4.13% and 8.46%, respectively, while Xuzhou, Zhengzhou, and Hefei saw an increase of 7.08%, 19.46%, and 41.57%, respectively. The comparison shows that PM2.5 is becoming less threatening in China and that ozone is becoming more difficult to control. Air pollution is a modifiable risk factor. Appropriate sustainable control policies are recommended to protect public health. [ABSTRACT FROM AUTHOR]

Fifteen cities across the world have been selected to investigate the public health co-benefits of PM2.5 reduction, during a period when various non-pharmaceutical interventions (NPIs) were adopted in the COVID-19 pandemic. Through applying a public health model, AirQ+, substantial spatial variations of global public health co-benefits were identified. Differences in seasonal air quality and population baselines were key underlying factors. For cities in North America, NPIs were introduced during the low pollution season, generating no co-benefits. On the other hand, tremendous health co-benefits were observed for cities in India and China, due to the high PM2.5 background with a large population. Among all, New Delhi has received the largest co-benefits, which saved over 14,700 premature deaths. As the pollution level (i.e., 45 μg m−3) with NPIs still exceeded the air quality standard, more rigorous emission controls are urgently needed to protect the public′s health in India. At last, a novel and practical tool for co-benefit screening was developed using data from one of the global measurement networks (i.e., IQAir). [ABSTRACT FROM AUTHOR]

Wet deposition is influencing air quality because air pollutants are washed away from the surrounding air. Consequently, particulate matter and associated compounds are transported in the rainwater and enter into soil, surface waters, and groundwater. Nonpoint sources of heavy metals from stormwater runoff have increased in urban areas due to industrialization and the increasing impervious surfaces. In this work, we present an assessment of the rainwater composition regarding the nutrients and other physicochemical characteristics measured in three locations selected in Targoviste city, Romania, a city that had a specialized steel factory and important metallurgical facilities. The rainwater was collected using three PALMEX rain samplers and then was transferred to high-density polyethylene bottles and analyzed using ICP-MS. PM2.5 concentrations were also monitored continuously using optical monitors calibrated using a gravimetric sampler. A detailed analysis of the heavy metals content in rainwater and PM was presented for the pollution episodes occurring in October and November 2019. Backward trajectories were computed using the HYSPLIT model for these periods. The results showed that the PM2.5 ranged from 11.1 to 24.1 μg/m3 in 2019, while the heavy metals in collected rainwater were (µg L−1): 0.25 (Cd) − CV = 26.5%, 0.10 (Co) − CV = 58.1%, 1.77 (Cr) − CV = 24.3%, 377.37 (Ni) − CV = 27.9%, 0.67 (Pb) − CV = 74.3%, and 846.5 (Zn) − CV = 20.6%. Overall, Ni, Pb, Cr, and V had significant correlations between the concentrations from rainwater and PM. Negative associations were found between precipitation events and heavy metals both from rainwater and PM, but only a few showed statistical significance. However, this could explain the "washing" effect of the rain on the heavy metals from PM2.5. The potential sources of nitrogen in the rainwater collected in Targoviste could be from burning fossil fuels and the soils, including both biological processes and fertilization resulting from the intensive agriculture in the piedmont plain in which the city is located. Based on the results, rainwater monitoring can constitute a reliable method for air quality characterization. Additional research is required to better understand seasonality and sources of heterogeneity regarding the associations between PM and rainwater composition. [ABSTRACT FROM AUTHOR]

In recent times, a significant number of studies on the composition and sources of fine particulate matters and volatile organic compounds have been carried out over Delhi, either initiated by or in association with the researchers from the Indian Institute of Technology Kanpur (IIT Kanpur), in collaboration with researchers from within and outside India. All these studies utilized highly time-resolved, campaign-mode observations made with state-of-the-art instrumentation during the late winter months (mid-January to March) of 2018. Individually, each of these studies were rigorous in nature, containing explicit detailing about different types of ambient air pollutants in Delhi such as organic aerosols, inorganic elements, metals, carbonaceous aerosols, and volatile organic compounds. This study consolidates the extremely useful knowledge on source attribution of these air pollutants in the Delhi National Capital Region currently contained in these fragmented studies, which is vital to further enhancing our understanding of composition, characteristics, and sources of air pollutants over Delhi, as well as to designing appropriate mitigation measures tailored to local specifics. [ABSTRACT FROM AUTHOR]

Dust and pollution are frequently mixed together in East Asia, causing large uncertainties in assessing climate change and environmental influence and in relevant policymaking. To discern the dust effect on particle mass, we carried out long-term measurements of the mass and key chemical compositions of PM10, PM2.5, and PM1 from August 2007 to February 2012 and collected hourly data of PM10 and PM2.5 concentrations from January 2012 to October 2020 at Gosan, South Korea. The principal component analysis of measured species reveals two dominant factors, pollution and dust, accounting for 46% and 16% of the total variance, respectively. The mode distribution of PM10, PM2.5, and PM1 mass in addition to the dust events helps to provide a robust criterion of the dust impact. Dust can be identified by the mean + standard deviation (σ) of PM10, while the threshold is down to the mean concentration when dust particles experience precipitation. High PM2.5 concentration also presents dust impact; however, the criterion decreases from mean + σ in 2007–2012 to mean in 2012–2020. It indicates that dust is no longer a high-concentration event of PM2.5, but its influence gradually appears in low-concentration particles. Therefore, the dust criterion obtained from long-term PM10 concentration data is robust; however, the standard is based on PM2.5 changes over time and still needs to be determined by follow-up long-term observations. [ABSTRACT FROM AUTHOR]

Air pollution has a significant impact on the use of solar energy. On the one hand, the polluted environment directly reduces the intensity of solar radiation, on the other hand, pollution of the environment will also reduce the life of the equipment. Therefore, mastering the dynamic characteristics of the atmospheric environment has certain guiding significance for the efficient utilization of solar energy. In this study, the concentrations of particulate matter, CO, SO2 and NO2 from Tianyun big data website are analyzed to obtain the general characteristics of particulate pollution in Wuhan. At the same time, a long-period sampling atmospheric particulate matter sampler in the Huangjiahu area of Wuhan has been used, and experimental analysis of the physical and chemical characteristics of the samples has been obtained. The results show that both PM2.5 and PM10 show obvious seasonal changes, and the concentration of the four anions during the sampling period is SO42− > NO3− > Cl− > F−. During the sampling period, atmospheric particulate matter is mainly composed of organic matter, inorganic anions and oxides of more than 20 elements. The results of the enrichment factor analysis show that elements such as Br, Pb, Sb and Zn are the main enriched elements during the sampling period. The enrichment factors of these elements are 246.43 ± 168.81, 133.28 ± 115.03, 403.305 ± 396.18 and 90.67 ± 67.01, respectively. The high enrichment of these elements also reflects the contribution of motor vehicle exhaust emission during the sampling period. Traffic source is the main emission source in the Huangjiahu area of Wuhan. This research has a certain guiding significance for many industries such as energy utilization, environmental monitoring, health care, transportation and so on. [ABSTRACT FROM AUTHOR]

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of "high–high" and "low–low". The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective. [ABSTRACT FROM AUTHOR]

Analysis of chemical composition characteristics of PM2.5 under different pollution degrees can reveal the changes of pollution sources. In order to make clear the evolution process of PM2.5 compositions in autumn and winter, PM2.5 samples were continuously collected and analyzed at Liaocheng city, China. The collected samples were classified as clean days (CLD), mild-moderate pollution days (MMD) and severe-serious pollution days (SSD). It was concluded that with the increase of pollution degrees, the concentrations of water-soluble ions and carbon components increased significantly, while elements only increased slightly. In addition, as the pollution degrees increased, the percentage of NO3−, SO42− and NH4+ increased significantly, from 23.0% in CLD to 49.0% in SSD, while the percentage of other components decreased, especially crust material. The PMF analyzed results showed that secondary transformation (36.7%), combustion sources (20.4%), secondary organic aerosols (SOA) (11.7%), vehicle sources (11%), dust (10.5%) and industrial processes (9.7%) were the main sources of PM2.5 during autumn and winter in Liaocheng. The contribution of secondary transformation reached 57% at the SSD level, which indicated that it was the main reason for the increase of PM2.5 concentrations. The air mass mainly came from five paths to Liaocheng. The secondary transformation contribution of the air mass with short transmission distance was higher, while the contribution of the dust was higher from the long distance. [ABSTRACT FROM AUTHOR]

Air pollution sources and the hazards of high particulate matter 2.5 (PM2.5) concentrations among air pollutants have been well documented. Shipping emissions have been identified as a source of air pollution; therefore, it is necessary to predict air pollutant concentrations to manage seaport air quality. However, air pollution prediction models rarely consider shipping emissions. Here, the PM2.5 concentrations of the Busan North and Busan New Ports were predicted using a recurrent neural network and long short-term memory model by employing the shipping activity data of Busan Port. In contrast to previous studies that employed only air quality and meteorological data as input data, our model considered shipping activity data as an emission source. The model was trained from 1 January 2019 to 31 January 2020 and predictions and verifications were performed from 1–28 February 2020. Verifications revealed an index of agreements (IOA) of 0.975 and 0.970 and root mean square errors of 4.88 and 5.87 µg/m3 for Busan North Port and Busan New Port, respectively. Regarding the results based on the activity data, a previous study reported an IOA of 0.62–0.84, with a higher predictive power of 0.970–0.975. Thus, the extended approach offers a useful strategy to prevent PM2.5 air pollutant-induced damage in seaports. [ABSTRACT FROM AUTHOR]

There is a well-documented association between ambient fine particulate matter air pollution (PM2.5) and cardiovascular disease (CVD) morbidity and mortality. Exposure to PM2.5 can cause premature death and harmful and chronic health effects such as heart attack, diabetes, and stroke. The Environmental Protection Agency sets annual PM2.5 standards to reduce these negative health effects. Currently above an annual average level of 12.0 µg/m is considered unhealthy. Methods. We examined the association of long-term exposure to PM2.5 and CVD in a cohort of 44,610 individuals who resided in 12 states recruited into the Southern Community Cohort Study (SCCS). The SCCS was designed to recruit Black and White participants who received care from Federally Qualified Health Centers; hence, they represent vulnerable individuals from low-income families across this vast region. This study tests whether SCCS participants who lived in locations exposed to elevated ambient levels of PM2.5 concentrations were more likely to report a history of CVD at enrollment (2002–2009). Remotely sensed satellite data integrated with ground monitoring data provide an assessment of the average annual PM2.5 in urban and rural locations where the SCCS participants resided. We used multilevel logistic regression to estimate the associations between self-reported CVD and exposure to elevated ambient levels of PM2.5. Results. We found a 13.4 percent increase in the odds of reported CVD with exposure to unhealthy levels of PM2.5 exposure at enrollment. The SCCS participants with medical histories of hypertension, hypercholesterolemia, and smoking had, overall, 385 percent higher odds of reported CVD than those without these clinical risk factors. Additionally, Black participants were more likely to live in locations with higher ambient PM2.5 concentrations and report high levels of clinical risk factors, thus, they may be at a greater future risk of CVD. Conclusions: In the SCCS participants, we found a strong relation between exposures to high ambient levels of PM2.5 and self-reported CVD at enrollment. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) has a serious impact on human health. Forecasting PM2.5 levels and analyzing the pollution sources of PM2.5 are of great significance. In this study, the Lagrangian particle dispersion (LPD) model was developed by combining the FLEXPART model and the Bayesian inventory optimization method. The LPD model has the capacity for real-time forecasting and determination of pollution sources of PM2.5, which refers to the contribution ratio and spatial distribution of each type of pollution (industry, power, residential, and transportation). In this study, we applied the LPD model to the Beijing-Tianjin-Hebei (BTH) region to optimize the a priori PM2.5 emission inventory estimates during 15–20 March 2018. The results show that (1) the a priori estimates have a certain degree of overestimation compared with the a posteriori flux of PM2.5 for most areas of BTH; (2) after optimization, the correlation coefficient (R) between the forecasted and observed PM2.5 concentration increased by an average of approximately 10%, the root mean square error (RMSE) decreased by 30%, and the IOA (index of agreement) index increased by 16% at four observation sites (Aotizhongxin_Beijing, Beichenkejiyuanqu_Tianjin, Dahuoquan_Xintai, and Renmingongyuan_Zhangjiakou); and (3) the main sources of pollution at the four sites mainly originated from industrial and residential emissions, while power factory and transportation pollution accounted for only a small proportion. The concentration of PM2.5 forecasts and pollution sources in each type of analysis can be used as corresponding reference information for environmental governance and protection of public health. [ABSTRACT FROM AUTHOR]

Microorganisms existing in airborne fine particulate matter (PM2.5) have key implications in biogeochemical cycling and human health. In this study, PM2.5 samples, collected in the typical basin cities of Xi'an and Linfen, China, were analyzed through high-throughput sequencing to understand microbial seasonal variation characteristics and ecological functions. For bacteria, the highest richness and diversity were identified in autumn. The bacterial phyla were dominated by Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. Metabolism was the most abundant pathway, with the highest relative abundance found in autumn. Pathogenic bacteria (Pseudomonas, Acinetobacter, Serratia, and Delftia) were positively correlated with most disease-related pathways. Besides, C cycling dominated in spring and summer, while N cycling dominated in autumn and winter. The relative abundance of S cycling was highest during winter in Linfen. For fungi, the highest richness was found in summer. Basidiomycota and Ascomycota mainly constituted the fungal phyla. Moreover, temperature (T) and sulfur dioxide (SO2) in Xi'an, and T, SO2, and nitrogen dioxide (NO2) in Linfen were the key factors affecting microbial community structures, which were associated with different pollution characteristics in Xi'an and Linfen. Overall, these results provide an important reference for the research into airborne microbial seasonal variations, along with their ecological functions and health impacts. [ABSTRACT FROM AUTHOR]

Sharp GP2Y1010 dust sensors are increasingly being used within distributed sensing networks and for personal monitoring of exposure to particulate matter (PM) pollution. These dust sensors offer an easy-to-use solution at an excellent price point; however, the sensors are known to offer limited dynamic range and poor limits of detection (L.O.D.), often >15 μg m−3. The latter figure of merit precludes the use of this inexpensive line of dust sensors for monitoring PM2.5 levels in environments within which particulate pollution levels are low. This manuscript presents a description of the fabrication and circuit used in the Sharp GP2Y1010 dust sensor and reports several effective strategies to minimize noise and maximize limits of detection for PM. It was found that measurement noise is primarily introduced within the photodiode detection circuitry, and that electromagnetic interference can influence dust sensor signals dramatically. Through optimization of the external capacitor and resistor used in the LED drive circuit—and the inter-pulse delay, electromagnetic shielding, and data acquisition strategy—noise was reduced approximately tenfold, leading to a projected noise equivalent limit of detection of 3.1 μg m−3. Strategies developed within this manuscript will allow improved limits of detection for these inexpensive sensors, and further enable research toward unraveling the spatial and temporal distribution of PM within buildings and urban centers—as well as an improved understanding of effect of PM on human health. [ABSTRACT FROM AUTHOR]

Ship-exhausted air pollutants could cause negative impacts on air quality, climate change, and human health. Increasing attention has been paid to investigate the impact of ship emissions on air quality. However, the conclusions are often based on a specific year, the extent to which the inter-annual variation in meteorological conditions affects the contribution is not yet fully addressed. Therefore, in this study, the Weather Research and Forecast model and the Community Multiscale Air Quality model(WRF/CMAQ) were employed to investigate the inter-annual variations in ship-contributed PM2.5 from 2010 to 2019. The Yangtze River Delta (YRD) region in China was selected as the target study area. To highlight the impact of inter-annual meteorological variations, the emission inventory and model configurations were kept the same for the 10-year simulation. We found that: (1) inter-annual meteorological variation had an evident impact on the ship-contributed PM2.5 in most coastal cities around YRD. Taking Shanghai as an example, the contribution varied between 3.05 and 5.74 µg/m3, with the fluctuation rate of ~65%; (2) the inter-annual changes in ship's contribution showed a trend of almost simultaneous increase and decrease for most cities, which indicates that the impact of inter-annual meteorological variation was more regional than local; (3) the inter-annual changes in the northern part of YRD were significantly higher than those in the south; (4) the most significant inter-annual changes were found in summer, followed by spring, fall and winter. [ABSTRACT FROM AUTHOR]

In order to investigate the chemical characteristics of aerosol pollution including PM1 and PM2.5 in Handan, the offline sampling campaign was conducted and the concentrations of total water-soluble inorganic ions (TWSI), carbonaceous components (OC and EC) were analyzed. The average concentrations were 88.5 μg/m3 for PM1 and 122 μg/m3 for PM2.5, and the corresponding ratios of PM1 versus PM2.5 on non-pollution, mild-moderate pollution and heavy pollution were 0.67, 0.70 and 0.77, respectively. TWSI and OC accounted for 43.2% and 15.4% in PM1, 41.8% and 16.0% in PM2.5. Secondary components in PM2.5 and PM1 increased with heavy pollution, SNA (SO42−, NO3− and NH4+) was enriched in PM1 but SOC (Secondary Organic Carbon) was more enriched in PM1–2.5. Furthermore, for evaluating the effect of aerosol feedback the WRF-Chem model was applied to identify the aerosol-radiation interaction of aerosol feedback influence on the PM2.5 concentration and various meteorological factors in Handan. The results indicated that the aerosol radiative effects will result in an average 32.62%(36.18 W/m2) decrease in downward short wave flux at ground surface (SWDOWN), an average 17.52% (39.15 m) and 0.16% (0.44 K) decrease in planetary boundary layer height(PBLH) and surface temperature (T2). The wind speed at 10 m (WS) and relative humidity (RH) will be increased by about 4.16%(0.11 m/s) and 1.89% (0.78%), respectively. [ABSTRACT FROM AUTHOR]

The severity of high atmospheric pollution has been a major social problem in northern China. To improve the air quality in the Beijing–Tianjin–Hebei (BTH) region and guarantee a suitable environment during the military parade and other celebrating activities for the 70th anniversary of the victory for anti-Fascist Warcraft in the year 2015, a series of strict air quality control policies were carried out. To analyze the reduction extents of PM2.5 and organic matter components during the control period and to examine the meteorological conditions in this region and their decisive influence on the air quality, PM2.5 samples were collected and the Lagrangian particle dispersion model FLEXPART was performed to calculate potential source locations within the BTH region. PM2.5, organic carbon (OC), elementary carbon (EC), and three species types were specifically analyzed. Although the results showed that PM2.5, OC, and EC reduced by 64.55%, 48.74%, and 60.75% during the control period, the air mass transport patterns showed great difference at certain periods, which altered the dominant transport direction of air mass and the potential source region of pollutants and organic matters. This alteration completely changed major atmospheric pollutants sources contribution and caused huge concentration changes. Parallel cases also showed that meteorological conditions could avoid massive atmospheric transported from a major emission source region to a receptor site. The meteorological conditions changed the main contribution source region in control and non-control periods and proved the air quality control measures were less necessary in some southern Hebei cities during special events periods. [ABSTRACT FROM AUTHOR]

In order to investigate the seasonal variations in the chemical characteristics of PM2.5 at the plateau slope of a mountain city in southwest China, 178 PM2.5 filters (89 quartz and 89 Teflon samples for PM2.5) were collected to sample the urban air of Wenshan in spring and autumn 2016 at three sites. The mass concentrations, water-soluble inorganic ions, organic and inorganic carbon concentrations, and inorganic elements constituting PM2.5 were determined, principal component analysis was used to identify potential sources of PM2.5, and the backward trajectory model was used to calculate the contribution of the long-distance transmission of air particles to the Wenshan area. The average concentration of PM2.5 in spring and autumn was 44.85 ± 10.99 μg/m3. Secondary inorganic aerosols contributed 21.82% and 16.50% of the total PM2.5 in spring and autumn, respectively. The daily mean value of OC/EC indicated that the measured SOC content was generated by the photochemical processes active during the sampling days. However, elements from anthropogenic sources (Ti, Si, Ca, Fe, Al, K, Mg, Na, Sb, Zn, P, Pb, Mn, As and Cu) accounted for 99.38% and 99.24% of the total inorganic elements in spring and autumn, respectively. Finally, source apportionment showed that SIA, dust, industry, biomass burning, motor vehicle emissions and copper smelting emissions constituted the major components in Wenshan. This study is the first to investigate the chemical characterizations and sources of PM2.5 in Wenshan, and it provides effective support for local governments formulating air pollution control policies. [ABSTRACT FROM AUTHOR]

With the current COVID-19 pandemic being spread all over the world, lockdown measures are being implemented, making air pollution levels go down in several countries. In this context, the air quality changes in the highly populated and trafficked Brazilian states of São Paulo (SP) and Rio de Janeiro (RJ) were addressed using a combination of satellite and ground-based daily data analysis. We explored nitrogen dioxide (NO 2 ) and fine particulate matter (PM 2.5 ) daily levels for the month of May from 2015–2020. Daily measurements of NO 2 column concentrations from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite were analyzed and decreases of 42% and 49.6% were found for SP and RJ, respectively, during the year 2020 compared to the 2015–2019 average. Besides NO 2 column retrievals, ground-based data measured by the Brazilian States Environmental Institutions were analyzed and correlated with satellite retrievals. Correlation coefficients between year-to-year changes in satellite column and ground-based concentrations were 77% and 53% in SP and RJ, respectively. Ground-based data showed 13.3% and 18.8% decrease in NO 2 levels for SP and RJ, respectively, in 2020 compared to 2019. In SP, no significant change in PM 2.5 was observed in 2020 compared to 2019. To further isolate the effect of emissions reduction due to the lockdown, meteorological data and number of wildfire hotspots were analyzed. NO 2 concentrations showed negative and positive correlations with wind speed and temperature, respectively. PM 2.5 concentration distributions suggested an influence by the wildfires in the southeast region of the country. Synergistic analyses of satellite retrievals, surface level concentrations, and weather data provide a more complete picture of changes to pollutant levels. [ABSTRACT FROM AUTHOR]

Pollutants emission, meteorological conditions, secondary formation, and pollutants transport are the main reasons for air pollution. A comprehensive air pollution analysis was conducted from the above four aspects in the autumn–winter seasons of 2017–2018 and 2018–2019 at Xingtai, China. In addition, the relationship between PM2.5 and O3 was also studied from the aspects of secondary formation and meteorological conditions to find the rules of cooperative management of PM2.5 and O3 combined pollution. Taking measures of concentrated and clean heating and controlling biomass burning could make the concentrations of EC, K+ and SO42− decrease. The variation trends of PM2.5 and O3 concentration in the autumn–winter season of Xingtai were different, and with the increase in secondary formation effects, the concentration of O3 decreased. Furthermore, the key meteorological conditions that affected O3 and PM2.5 formation were temperature and relative humidity, respectively. The relationships of NOR (nitrate oxidation rate) and SOR (sulfate oxidation rate) against temperature presented a "U" shape, suggesting that gas-phase oxidation and gas–solid-phase oxidation were all suppressed at a temperature of around 4 °C. The cities located in the east had more pollutant transporting effects during the pollution processes of Xingtai, and the main transport routes of O3 and PM2.5 were not all the same. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic poses a serious global threat to human health. In China, the government immediately implemented lockdown measures to curb the spread of this virus. These measures severely affected transportation and industrial production across the country, resulting in a significant change in the concentration of air pollutants. In this study, the Euclidean distance method was used to select the most similar meteorological field during the COVID-19 lockdown period. Changes in the concentration of air pollutants in China were analyzed under similar meteorological background conditions. Results indicate that, compared with data from 2015–2019, air quality in China significantly improved; with the exception of ozone (O3), the concentration of major air pollutants declined. Compared with baseline conditions, the reduction of air pollutants in China from 25 January to 22 February 2020 (Period 2) was the most significant. In particular, NO2 decreased by 41.7% in the Yangtze River Delta. In Period 2, the reduction of air pollutants in areas other than Hubei gradually decreased, but the reduction of NO2 in Wuhan reached 61.92%, and the reduction of air pollutants in various regions after February 23 was significantly reduced. By excluding the influence of meteorological factors and calculating the contribution of human activities to atmospheric pollutants by linear fitting, in Period 2 the effect of artificial controls on NO2 in Wuhan attained 30.66%, and reached 48.17% from 23 February to 23 March (Period 3). Results from this investigation provides effective theoretical support for pollution prevention and control in China. [ABSTRACT FROM AUTHOR]

In this study, the ionic and carbonaceous compounds in PM2.5 were analysed in the small residential city of Chuncheon, Korea. To identify the local sources that substantially influence PM2.5 concentrations, the samples were divided into two groups: samples with PM2.5 concentrations higher than those in the upwind metropolitan area (Seoul) and samples with lower PM2.5 concentrations. During the sampling period (December 2016–August 2018), the average PM2.5 was 23.2 μg m−3, which exceeds the annual national ambient air quality standard (15 μg m−3). When the PM2.5 concentrations were higher in Chuncheon than in Seoul, the organic carbon (OC) and elemental carbon (EC) concentrations increased the most among all the PM2.5 components measured in this study. This is attributable to secondary formation and biomass burning, because secondary OC was enhanced and water soluble OC was strongly correlated with K+, EC, and OC. A principal component analysis identified four factors contributing to PM2.5: fossil-fuel combustion, secondary inorganic and organic reactions in biomass burning plumes, crustal dust, and secondary NH4+ formation. [ABSTRACT FROM AUTHOR]

In the recent decade, the North China Plain (NCP) has been among the region's most heavily polluted by PM2.5 in China. For the nonattainment cities in the NCP, joint pollution control with related cities is highly needed in addition to the emission controls in their own cities. However, as the basis of decision-making, the spatial characteristics of PM2.5 among these cities are still insufficiently revealed. In this work, the spatial characteristics among all nonattainment cities in the northern part of the North China Plain (NNCP) region were revealed based on data mining technologies including clustering, coefficient of divergence (COD), network correlation model, and terrain and meteorology analysis. The results indicate that PM2.5 pollution of cities with a distance of less than 180 km exhibits homogeneity in the NCP region. Especially, the sub-region, composed of Xinxiang, Hebi, Kaifeng, Zhengzhou, and Jiaozuo, was strongly homogeneous and a strong correlation exists among them. Compared with spring and summer, much stronger correlations of PM2.5 between cities were found in autumn and winter, indicating a strong need for joint prevention and control during these periods. All nonattainment cities in this region were divided into city-clusters, depending on the seasons and pollution levels to further helping to reduce their PM2.5 concentrations effectively. Air stagnation index (ASI) analysis indicates that the strong correlations between cities in autumn were more attributed to the transport impacts than those in winter, even though there were higher PM2.5 concentrations in winter. These results provided an insight into joint prevention and control of pollution in the NCP region. [ABSTRACT FROM AUTHOR]

Accurate forecast of PM2.5 pollution is highly needed for the timely prevention of haze pollution in many cities suffered from frequent haze pollution. In this work, an online recurrent extreme learning machine (OR-ELM) technique with online data update was used in the forecast of PM2.5 pollution for the first time, and a hybrid model (OR-ELM-AR) by combining autoregressive (AR) model was proposed to enhance its forecast ability to capture the variations of hourly PM2.5 concentration. Evaluation of forecast performances in terms of pollution levels, forecast times, spatial distributions were conducted over the Yangtze River Delta (YRD) region, China. Results indicated that the OR-ELM-AR model could quickly respond to short-term changes and had better forecast performance. Therefore, the OR-ELM-AR model is a promising tool for air pollution forecast of supporting the government to take urgent actions to reduce the frequency and severity of haze pollution in cities or regions. [ABSTRACT FROM AUTHOR]

Air pollution is an important issue that can have significant implications for human health. Consequently, air quality control is matter of great interest, and the ΕU has established strict legislation with respect to public health protection. A work package of the EMPIR project AEROMET focused on the investigation of traceable validated methods for chemical composition of airborne particulate matter (PM), including heavy metals. Incineration ash typically contains quantities of heavy and toxic metals in excess of the limits imposed for airborne PM, so it provides a candidate source for a Standard Reference Material (SRM). In this work, a method for loading incinerator ash (PM10 and PM2.5 fractions) on quartz filters with a good reproducibility and homogeneity was developed. An intercomparison exercise involving three separate laboratories was conducted for the elemental analysis of the prepared candidate reference material. The filters were treated by acid digestion and analyzed by Inductively Coupled Plasma–Mass Spectrometry (ICP-MS) according to the European standard EN 14902:2005. Eleven elements, including the regulated metals (As, Cd, Ni and Pb), were determined using different ICP-MS instruments and standardization methods. Data evaluation showed a good agreement between the results, with deviations below 10–15%. The development of a Standard Reference Material seems auspicious. [ABSTRACT FROM AUTHOR]

Global assessments of air quality and health require comprehensive estimates of the exposures to air pollution that are experienced by populations in every country. However, there are many countries in which measurements from ground-based monitoring are sparse or non-existent, with quality-control and representativeness providing additional challenges. While ground-based monitoring provides a far from complete picture of global air quality, there are other sources of information that provide comprehensive coverage across the globe. The World Health Organization developed the Data Integration Model for Air Quality (DIMAQ) to combine information from ground measurements with that from other sources, such as atmospheric chemical transport models and estimates from remote sensing satellites in order to produce the information that is required for health burden assessment and the calculation of air pollution-related Sustainable Development Goals indicators. Here, we show an example of the use of DIMAQ with the Copernicus Atmosphere Monitoring Service Re-Analysis (CAMSRA) of atmospheric composition, which represents the best practices in meteorology and climate monitoring that were developed under the World Meteorological Organization's Global Atmosphere Watch programme. Estimates of PM2.5 from CAMSRA are integrated within the DIMAQ framework in order to produce high-resolution estimates of air pollution exposure that can be aggregated in a coherent fashion to produce country-level assessments of exposures. [ABSTRACT FROM AUTHOR]

Air pollution is recognized as the most important environmental factor that adversely affects human and societal wellbeing. Due to rapid urbanization, air pollution levels are increasing in the Sub-Saharan region, but there is a shortage of air pollution monitoring. Hence, exposure data to use as a base for exposure modelling and health effect assessments is also lacking. In this study, low-cost sensors were used to assess PM2.5 (particulate matter) levels in the city of Adama, Ethiopia. The measurements were conducted during two separate 1-week periods. The measurements were used to develop a land-use regression (LUR) model. The developed LUR model explained 33.4% of the variance in the concentrations of PM2.5. Two predictor variables were included in the final model, of which both were related to emissions from traffic sources. Some concern regarding influential observations remained in the final model. Long-term PM2.5 and wind direction data were obtained from the city's meteorological station, which should be used to validate the representativeness of our sensor measurements. The PM2.5 long-term data were however not reliable. Means of obtaining good reference data combined with longer sensor measurements would be a good way forward to develop a stronger LUR model which, together with improved knowledge, can be applied towards improving the quality of health. A health impact assessment, based on the mean level of PM2.5 (23 µg/m3), presented the attributable burden of disease and showed the importance of addressing causes of these high ambient levels in the area. [ABSTRACT FROM AUTHOR]

In order to fight against the spread of COVID-19, the most hard-hit countries in the spring of 2020 implemented different lockdown strategies. To assess the impact of the COVID-19 pandemic lockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the change in air quality in 20 major cities on six continents. Our results show significant declines of AQI in NO2, SO2, CO, PM2.5 and PM10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown. This work shows the reduction of primary pollutants, especially NO2, is mainly due to lockdown policies. However, preexisting local environmental policy regulations also contributed to declining NO2, SO2 and PM2.5 emissions, especially in Asian countries. In addition, higher rainfall during the lockdown period could cause decline of PM2.5, especially in Johannesburg. By contrast, the changes of AQI in ground-level O3 were not significant in most of cities, as meteorological variability and ratio of VOC/NOx are key factors in ground-level O3 formation. [ABSTRACT FROM AUTHOR]

Cluster analyses, potential source contribution function (PSCF) and concentration-weight trajectory (CWT) were used to identify the main transport pathways and potential source regions with hourly PM2.5 and PM10 concentrations in different seasons from January 2017 to December 2019 at Akedala Station, located in northwest China (Central Asia). The annual mean concentrations of PM2.5 and PM10 were 11.63 ± 9.31 and 19.99 ± 14.39 µg/m3, respectively. The air pollution was most polluted in winter, and the dominant part of PM10 (between 54 to 76%) constituted PM2.5 aerosols in Akedala. Particulate pollution in Akedala can be traced back to eastern Kazakhstan, northern Xinjiang, and western Mongolia. The cluster analyses showed that the Akedala atmosphere was mainly affected by air masses transported from the northwest. The PM2.5 and PM10 mainly came with air masses from the central and eastern regions of Kazakhstan, which are characterized by highly industrialized and semi-arid desert areas. In addition, the analyses of the pressure profile of back-trajectories showed that air mass distribution were mainly distributed above 840 hPa. This indicates that PM2.5 and PM10 concentrations were strongly affected by high altitude air masses. According to the results of the PSCF and CWT methods, the main potential source areas of PM2.5 were very similar to those of PM10. In winter and autumn, the main potential source areas with high weighted PSCF values were located in the eastern regions of Kazakhstan, northern Xinjiang, and western Mongolia. These areas contributed the highest PM2.5 concentrations from 25 to 40 µg/m3 and PM10 concentrations from 30 to 60 µg/m3 in these seasons. In spring and summer, the potential source areas with the high weighted PSCF values were distributed in eastern Kazakhstan, northern Xinjiang, the border between northeast Kazakhstan, and southern Russia. These areas contributed the highest PM2.5 concentrations from 10 to 20 µg/m3 and PM10 concentrations from 20 to 60 µg/m3 in these seasons. [ABSTRACT FROM AUTHOR]

High particulate matter (PM2.5) concentrations have been considered a serious environmental issue in South Korea. Recent studies have focused mostly on metropolitan and industrial cities; however, high PM2.5 episodes have also been frequently observed even in small– and middle-sized cities. Thus, in this study, PM2.5 and its major chemical components were measured in a small residential city with low anthropogenic emissions for 2 years to identify the factors affecting the PM2.5 concentrations. Overall, the average PM2.5 concentration was 29.4 μg m−3: about two times higher than the annual ambient air quality standard value. In winter, when the PM2.5 concentrations were generally higher, relative humidity (RH) was significantly correlated with both PM2.5 mass and the PM2.5/PM10 ratio, suggesting that high RH promoted the formation of secondary PM2.5. In addition, SO42− and NO3− were found to be correlated with both NH4+ and K+ in winter, indicating that biomass burning was an important source in this city. Water-soluble organic carbon (WSOC) was also highly correlated with elemental carbon (EC) and K+ in fall and winter, when the burning of agricultural residues actively occurred. During high concentration episodes, NO3− exhibited the highest increase; nevertheless, other components (e.g., K+ and organic carbon) also significantly increased. [ABSTRACT FROM AUTHOR]

This work studied the spread of COVID-19, the meteorological conditions and the air quality in a megacity from two viewpoints: (1) the correlation between meteorological and air quality (PM10 and NO2) variables with infections and deaths due COVID-19, and (2) the improvement in air quality. Both analyses were performed for the pandemic lockdown due to COVID-19 in the City of Buenos Aires (CABA), the capital and the largest city in Argentina. Daily data from temperature, rainfall, average relative humidity, wind speed, PM10, NO2, new cases and deaths due COVID-19 were analyzed. Our findings showed a significant correlation of meteorological and air quality variables with COVID-19 cases. The highest temperature correlation occurred before the confirmation day of new cases. PM10 presented the highest correlation within 13 to 15 days lag, while NO2 within 3 to 6 days lag. Also, reductions in PM10 and NO2 were observed. This study shows that exposure to air pollution was significantly correlated with an increased risk of becoming infected and dying due to COVID-19. Thus, these results show that the NO2 and PM10 levels in CABA can serve as one of the indicators to assess vulnerability to COVID-19. In addition, decision-makers can use this information to adopt strategies to restrict human mobility during the COVID-19 pandemic and future outbreaks of similar diseases in CABA. [ABSTRACT FROM AUTHOR]

Atmospheric aerosol pollution has significant impacts on human health and economic society. One of the most efficient way to remove the pollutants from the atmosphere is wet deposition. This study selected three typical atmospheric pollution regions in China, the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) regions, as research areas, and used the hourly precipitation and PM2.5 mass concentration data from 2015 to 2017 to investigate the removal impacts of precipitation on PM2.5. The PM2.5 mass concentration difference before and after the hourly precipitation events was used to denote as the impacts of precipitation. Hourly precipitation event was selected so that the time difference between two PM2.5 observations was short enough to limit the PM2.5 change caused by other factors. This study focused on the differences in the removal effect of precipitation on PM2.5 under different precipitation intensities and pollution levels. The results show that both precipitation intensity and aerosol amount affected the removal effect. A negative removal effect existed for both light precipitation and low PM2.5 mass concentration conditions. In contrast, a positive removal effect occurred for both high precipitation and high PM2.5 mass concentration conditions. The removal effect increased with increasing precipitation intensity and PM2.5 mass concentration before precipitation and was consistent with the change trend of wind speed at a height of 100 m. The findings of this study can help understand the mechanism of wet scavenging on air pollution, providing support for air pollution control in future. [ABSTRACT FROM AUTHOR]

Sulfate, nitrate and ammonium (SNA) are the dominant components of water-soluble ions (WSIs) in PM2.5, which are of great significance for understanding the sources and transformation mechanisms of PM2.5. In this study, daily PM2.5 samples were collected from September 2017 to August 2018 within the Guiyang urban area and the concentrations of the major WSIs in the PM2.5 samples were characterized. The results showed that the average concentration of SNA (SO42−, NO3−, NH4+) was 15.01 ± 9.35 μg m−3, accounting for 81.05% (48.71–93.76%) of the total WSIs and 45.33% (14.25–82.43%) of the PM2.5 and their possible chemical composition in PM2.5 was (NH4)2SO4 and NH4NO3. The highest SOR (sulfur oxidation ratio) was found in summer, which was mainly due to the higher temperature and O3 concentrations, while the lowest NOR (nitrogen oxidation ratio) found in summer may ascribe to the volatilization of nitrates being accelerated at higher temperature. Furthermore, the nitrate formation was more obvious in NH4+-rich environments so reducing NH3 emissions could effectively control the formation of nitrate. The results of the trajectory cluster analysis suggested that air pollutants can be easily enriched over short air mass trajectories from local emission sources, affecting the chemical composition of PM2.5. [ABSTRACT FROM AUTHOR]

Ambient reactive nitrogen is a mix of nitrogen-containing organic and inorganic compounds. These various compounds are found in both aerosol- and gas-phases with oxidized and reduced forms of nitrogen. Aerosol-phase reduced nitrogen is predominately thought to include ammonium and amines. In ambient samples, the ammonium concentration is routinely determined, but the contribution of amines is not. We developed a method to discretely measure amines from ambient aerosol samples. It employs ion chromatography using a Thermo Scientific IonPac Dionex CS-19 column with conductivity detection and a three-step separation using a methanesulfonic acid eluent. This method allows for the quantification of 18 different amines, including the series of methylamines and the different isomers of butylamine. Almost all amines quantifiable by this technique were measured regularly when applying this method to ambient filter samples collected in Rocky Mountain National Park (RMNP) and Greeley, CO. The sum of the amines was ~0.02 µg m−3 at both sites. This increased to 0.04 and 0.09 µg m−3 at RMNP and Greeley, respectively, at the same time they were impacted by smoke. Analysis of separate, fresh biomass burning source samples, however, suggests that smoke is likely a minor emission source of amines in most environments. [ABSTRACT FROM AUTHOR]

Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest. [ABSTRACT FROM AUTHOR]

This article focuses on the source apportionment of air pollution in a specific northeastern part of the Czech Republic. The research area, located around the city of Třinec, is significantly affected by a complex spectrum of air pollution sources, including local residential heating (coal and wood burning), heavy industry (mainly iron and steel production), road traffic, and regional and long-range air pollution transport from the nearby cities, Poland, and other countries. The main pollution sources contributing to the total concentration of fine suspended particles (PM2.5) were evaluated on the basis of the measurements at three sites and on subsequent positive matrix factorization modeling. The six major air pollution factors were identified, and their relative and absolute contributions were quantified. The result of the study is that the most important current task of air protection is to reduce the residential emissions from solid fuels, which are responsible for approximately 50–60% of PM2.5 concentration, followed by the regional primary and secondary aerosol sources (up to 40% of the total PM2.5 aerosol mass). Lower contributions have been identified in the case of resuspended mineral and biogenic particles (15–20%), long-range (trans-European) air pollution transport (up to 10%), and heavy industry (up to 10% in the most affected location). A detailed discussion has been provided considering specific regional EC (elemental carbon)–OC (organic carbon) relations in the region with traditional coal-burning for household heating which complicate the interpretation of the PMF (Positive Matrix Factorization) results, especially due to the interference between the traffic, residential heating, and biogenic aerosol factors. [ABSTRACT FROM AUTHOR]

To accurately apportion the sources of aerosols, a combined method of positive matrix factorization (PMF) and the Bayesian mixing model was applied in this study. The PMF model was conducted to identify the sources of PM2.5 in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (δ15N-NO3− and δ18O-NO3−), the Bayesian mixing model was performed to apportion the source of NO3− to coal combustion, traffic emission and biogenic source. Then the secondary aerosol source was subdivided into three sources according to the discrepancy in source apportionment of NO3− between PMF and Bayesian mixing model results. After secondary aerosol assignment, the six main sources of PM2.5 were traffic emission (30.6%), biomass burning (23.1%), coal combustion (17.7%), ship emission (14.0%), biomass boiler (9.9%) and industrial emission (4.7%). To assess the source apportionment results, fossil/non-fossil source contributions to organic carbon (OC) and element carbon (EC) inferred from 14C measurements were compared with the corresponding results in the PMF model. The results showed that source distributions of EC matched well between those two methods, indicating that the PMF model captured the primary sources well. Probably because of the lack of organic molecular markers to identify the biogenic source of OC, the non-fossil source contribution to OC in PMF results was obviously lower than 14C results. Thus, an indicative organic molecular tracer should be used to identify the biogenic source when accurately apportioning the sources of aerosols, especially in the region with high plant coverage or intense biomass burning. [ABSTRACT FROM AUTHOR]

An extensive field campaign was carried out in Florence (Tuscany) to investigate the PM2.5 composition and to identify its sources. The scientific objective of this study is providing a reliable source apportionment, which is mandatory for the application of effective mitigation actions. Particulate matter (PM) was collected for one year, simultaneously in a traffic site, in an urban background, and in a regional background site. While the use of two filter types (quartz and Teflon) allowed obtaining a comprehensive chemical characterization (elemental and organic carbon, ions, elements) by the application of different analytical techniques, the location of the three sampling sites allowed getting a better separation among local, urban, regional and transboundary sources. During shorter periods, the aerosol was also collected by means of a streaker sampler and PIXE (Particle Induced X-ray Emission) analysis of these samples allowed the assessment of hourly resolution elemental time trends. Positive matrix factorisation (PMF) identified seven main sources: traffic, biomass burning, secondary sulphate, secondary nitrates, urban dust, Saharan dust and marine aerosol. Traffic mass concentration contributions were found to be strong only at the traffic site (~8 μg·m−3, 33% of PM2.5). Biomass burning turned out to be an important PM2.5 source in Florence (~4 μg·m−3), with very similar weights in both city sites while at the regional background site its weight was negligible. Secondary sulphate is an important PM2.5 source on a regional scale, with comparable values in all three sites (~3.5 μg·m−3). On average, the contribution of the "natural" components (e.g., mineral dust and marine aerosols) to PM2.5 is moderate (~1 μg·m−3) except during Saharan dust intrusions where this contribution is higher (detected simultaneously in all three sites). High-time resolution data confirmed and reinforced these results. [ABSTRACT FROM AUTHOR]

As one of the biggest cities in North China, Jinan has been suffering heavy air pollution in recent decades. To better characterize the ambient particulate matter in Jinan during heavy pollution periods, we collected daily PM2.5 (particulate matter with aerodynamic diameters equal to or less than 2.5 μm) filter samples from 15 October 2017 to 31 January 2018 and analyzed their chemical compositions (including inorganic water-soluble ions (WSIs), carbonaceous species, and inorganic elements). The daily average concentration of PM2.5 was 83.5 μg/m3 during the sampling period. A meteorological analysis revealed that both low wind speed and high relative humidity facilitated the occurrence of high PM2.5 pollution episodes. A chemical analysis indicated that high concentrations of water-soluble ions, carbonaceous species, and elements were observed during heavy pollution days. The major constituents of PM2.5 in Jinan were secondary aerosol particles and organic matter based on the results of mass closure. Chemical Mass Balance (CMB) was used to track possible sources and identified that nitrate, sulfate, vehicle exhaust and coal fly ash were the main contributors to PM2.5 during heavy pollution days in Jinan, accounting for 25.4%, 18.6%, 18.2%, and 13.3%, respectively. [ABSTRACT FROM AUTHOR]