Simulation model is very essential for predicting the environmental fate and the potential environmental consequences of chemical pollutants including those from accidental chemical spills. However very few of such simulation model is seen related to Chinese costal water body. As the first step toward our final goal to develop a simulation model for the prediction and the risk assessment of chemical pollutants in Chinese coastal water, this study developed a three-dimensional (3D) hydrodynamic model of Xiamen Bay (XMB). This hydrodynamic model was externally derived by meteorological data, river discharge and boundary conditions of XMB. We used the model to calculate the physical factors, especially water temperature, salinity and flow field, from June to September 2016 in XMB. The results demonstrated a good match between observations and simulations, which underscores the feasibility of this model in predicting the spatial-temporal concentration of chemical pollutants in the coastal water of XMB. Longitudinal salinity distributions and the mixing profile of river-sea interactions are discussed, including the obvious gradation of salinity from the river towards sea sites shown by the model. We further assumed that 1000 kg and 1000 mg/L of a virtual chemical pollutant leaked out from Jiulong River (JR) estuary (point source) and whole XMB (non-point source), respectively. The model illustrates that it takes three months for XMB to become purified when point source pollution occurs in the estuary, while half a year to be required in the case of non-point source pollution across the entire bay. Moreover, the model indicated that pollutants can easily accumulate in the western coastal zone and narrow waters like Maluan Bay, which can guide environmental protection strategies.

This study employs the grain size distributions and the concentrations and isotopic compositions of Sr, Nd, and Pb in the precipitation samples collected from the Laohugou Glacier (LHG) in northeastern Tibetan Plateau (TP) during August 2014–2015 to investigate seasonal variability in the insoluble precipitation particle sources. Fine dust particle (0.57–27 μm) depositions dominated in autumn and winter, whereas both fine and coarse dust particle (27–100 μm) depositions were found in spring and summer. Furthermore, the concentrations of Sr, Nd, and Pb also varied seasonally—the highest and lowest Sr and Nd concentrations were recorded in spring and autumn, respectively, whereas the highest and lowest Pb concentrations were recorded in winter and summer, respectively. The Sr and Nd isotopes revealed that the dust in the winter precipitation originated predominately from the Taklimakan Desert and that in spring originated from the Badain Jaran and Qaidam deserts. The precipitation residues in summer were derived from a complex mixture of dust sources from the Gobi and other large deserts in northwest China. Autumn residues were predominately sourced from local soil near the LHG as well as from the Qaidam Basin and the northern TP surface soil. The Taklimakan, long suspected as a major source of long-range transported dust, was an insignificant contributor to the precipitation over LHG during spring, summer, and autumn. Further, the Pb isotopic ratios indicated a primary impact of anthropogenic pollutants for most part of the year (except spring). Meteorological data and the MODIS AOD model are in good agreement with the results from the analyses of the Sr, Nd, and Pb isotopes for the LHG particle source, and further clarify the source regions. Thus, this study thus provides new evidence on the seasonal variability of the sources of the residual particles in remote glaciers in Central Asia.

Initiatives to displace petroleum and climate change mitigation have driven a recent increase in space heating with biomass combustion. However, there is ample evidence that biomass combustion emits significant quantities of health damaging pollutants. We investigated the near-source micro-environmental air quality impact of a biomass-fueled combined heat and power system equipped with an electrostatic precipitator (ESP) in Syracuse, NY. Two rooftop sampling stations with PM2.5 and CO2 analyzers were established in such that one could capture the plume while the other one served as the background for comparison depending on the wind direction. Four sonic anemometers were deployed around the stack to quantify spatially and temporally resolved local wind patterns. Fuel-based emission factors were derived based on near-source measurement. The Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model was then applied to simulate the spatial variations of primary PM2.5 without ESP. Our analysis shows that the absence of ESP could lead to an almost 7 times increase in near-source primary PM2.5 concentrations with a maximum concentration above 100 μg m−3 at the building rooftop. The above-ground “hotspots” would pose potential health risks to building occupants since particles could penetrate indoors via infiltration, natural ventilation, and fresh air intakes on the rooftop of multiple buildings. Our results demonstrated the importance of emission control for biomass combustion systems in urban area, and the need to take above-ground pollutant “hotspots” into account when permitting distributed generation. The effects of ambient wind speed and stack temperature, the suitability of airport meteorological data on micro-environmental air quality were explored, and the implications on mitigating near-source air pollution were discussed.

This study investigated whether the rotavirus infection rate in children is associated with temperature and air pollutants in Hangzhou, China. This study applied a distributed lag non-linear model (DLNM) to assess the effects of daily meteorological data and air pollutants on the rotavirus positive rate among outpatient children. There was a negative correlation between temperature and the rotavirus infection rate. The impact of temperature on the detection rate of rotavirus presented an evident lag effect, the temperature change shows the greatest impact on the detection rate of rotavirus approximate at lag one day, and the maximum relative risk (RR) was approximately 1.3. In 2015, the maximum cumulative RR due to the cumulative effect caused by the temperature drop was 2.5. Particulate matter (PM) 2.5 and PM10 were the primary air pollutants in Hangzhou. The highest RR of rotavirus infection occurred at lag 1–1.5 days after the increase in the concentration of these pollutants, and the RR increased gradually with the increase in concentration. Based on the average concentrations of PM2.5 of 53.9 μg/m3 and PM10 of 80.6 μg/m3 in Hangzhou in 2015, the cumulative RR caused by the cumulative effect was 2.5 and 2.2, respectively. The current study suggests that temperature is an important factor impacting the rotavirus infection rate of children in Hangzhou. Air pollutants significantly increased the risk of rotavirus infection, and dosage, lag and cumulative effects were observed.

The past decade marked record high air pollution episodes in Indonesia. In this study, we specifically focus on vegetation fires in Palangkaraya located near a Mega Rice Project area in Indonesia. We analyzed various gaseous air pollution data such as particulate matter (PM10), SO2, CO, O3, and NO2 study region. We also conducted elemental analysis at two different sites. Results from 2001 to 2010 suggested the longest hazardous air pollution episode during 2002 lasting about 80 days from mid-August to late-October. Maximum peak concentrations of PM10, SO2, CO, and O3 were also observed during 2002 and their values reached 1905, 85.8, 38.3, and 1003 × 10−6 gm−3 respectively. Elemental analysis showed significant increase in concentrations during 2011 and 2010. Satellite retrieved fires and weather data could explain most of the temporal variations. Our results highlight peat fires as a major contributor of photochemical smog and air pollution in the region.

This study aimed to investigate the effects of dust–haze on mortality and to estimate the seasonal and individual-specific modification effects in Guangzhou, China. Mortality, air pollution and meteorological data were collected for 2006–2011. A dust–haze day was defined as daily visibility <10 km with relative humidity <90%. This definition was further divided into light (8–10 km), medium (5–8 km) and heavy dust–haze (<5 km). A distributed lag linear model (DLM) was employed. Light, medium and heavy dust–haze days were associated with increased mortality of 3.4%, 6.8% and 10.4% respectively, at a lag of 0–6 days. This effect was more pronounced during the cold season, for cardiovascular mortality (CVD), respiratory mortality (RESP), in males and people ≥60years. These effects became insignificant after adjustment for PM10. We concluded that dust–haze significantly increased mortality risk in Guangzhou, China, and this effect appears to be dominated by particulate mass and modified by season and individual-specific factors.

Arable soils are a significant source of nitric oxide (NO), a precursor of tropospheric ozone, and thereby contribute to ozone pollution. However, their actual impact on ozone formation is strongly related to their spatial and temporal emission patterns, which warrant high-resolution estimates. Here, we combined an agro-ecosystem model and geo-referenced databases to map these sources over the 12 000 km2 administrative region surrounding Paris, France, with a kilometric level resolution. The six most frequent arable crop species were simulated, with emission rates ranging from 1.4 kg N–NO ha−1 yr−1 to 11.1 kg N–NO ha−1 yr−1. The overall emission factor for fertilizer-derived NO emissions was 1.7%, while background emissions contributed half of the total NO efflux. Emissions were strongly seasonal, being highest in spring due to fertilizer inputs. They were mostly sensitive to soil type, crops' growing season and fertilizer N rates. The use of an agro-ecosystem model at regional scale makes it possible to map the emissions of nitric oxide from arable soils at a resolution compatible with tropospheric ozone models.

It is enlightening to determine the discrepancies and potential reasons for the degree of impact from the COVID-19 control measures on air quality as well as the associated health and economic impacts. Analysis of air quality, socio-economic factors, and meteorological data from 447 cities in 46 countries indicated that the COVID-19 control measures had significant impacts on the PM₂.₅ (particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations in 20 (reduced PM₂.₅ concentrations of −7.4–29.1 μg m⁻³) of the selected 46 countries. In these 20 countries, the robustly distinguished changes in the PM₂.₅ concentrations caused by the control measures differed between the developed (95% confidence interval (CI): −2.7–5.5 μg m⁻³) and developing countries (95% CI: 8.3–23.2 μg m⁻³). As a result, the COVID-19 lockdown reduced death and hospital admissions change from the decreased PM₂.₅ concentrations by 7909 and 82,025 cases in the 12 developing countries, and by 78 and 1214 cases in the eight developed countries. The COVID-19 lockdown reduced the economic cost from the PM₂.₅ related health burden by 54.0 million dollars in the 12 developing countries and by 8.3 million dollars in the eight developed countries. The disparity was related to the different chemical compositions of PM₂.₅. In particular, the concentrations of primary PM₂.₅ (e.g., BC) in cities of developing countries were 3–45 times higher than those in developed countries, so the mass concentration of PM₂.₅ was more sensitive to the reduced local emissions in developing countries during the COVID-19 control period. The mass fractions of secondary PM₂.₅ in developed countries were generally higher than those in developing countries. As a result, these countries were more sensitive to the secondary atmospheric processing that may have been enhanced due to reduced local emissions.

People with chronic obstructive pulmonary disease, cardiovascular disease, or hypertension have a high risk of developing severe coronavirus disease 2019 (COVID-19) and of COVID-19 mortality. However, the association between long-term exposure to air pollutants, which increases cardiopulmonary damage, and vulnerability to COVID-19 has not yet been fully established. We collected data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China. We fitted a generalized linear model using city-level COVID-19 cases and severe cases as the outcome, and long-term average air pollutant levels as the exposure. Our analysis was adjusted using several variables, including a mobile phone dataset, covering human movement from Wuhan before the travel ban and movements within each city during the period of the emergency response. Other variables included smoking prevalence, climate data, socioeconomic data, education level, and number of hospital beds for 324 cities in China. After adjusting for human mobility and socioeconomic factors, we found an increase of 37.8% (95% confidence interval [CI]: 23.8%–52.0%), 32.3% (95% CI: 22.5%–42.4%), and 14.2% (7.9%–20.5%) in the number of COVID-19 cases for every 10-μg/m³ increase in long-term exposure to NO₂, PM₂.₅, and PM₁₀, respectively. However, when stratifying the data according to population size, the association became non-significant. The present results are derived from a large, newly compiled and geocoded repository of population and epidemiological data relevant to COVID-19. The findings suggested that air pollution may be related to population vulnerability to COVID-19 infection, although the extent to which this relationship is confounded by city population density needs further exploration.

Northern China is a significant source of dust source in Central Asia. Thus, high-resolution analysis of dust storms and comparison of dust sources in different regions of northern China are important to clarify the formation mechanism of East Asian dust storms and predict or even prevent such storms. Here, we analyzed spatiotemporal trends in dust storms that occurred in three main dust source regions during 1960–2007: Taklimakan Desert (western region [WR]), Badain Jaran and Tengger Deserts (middle region [MR]), and Otindag Sandy Land (eastern region [ER]). We analyzed daily dust storm frequency (DSF) at the 10-day scale (first [FTDM], middle [MTDM], and last [LTDM] 10 days of a month), and investigated the association of dust storm occurrences with meteorological factors. The 10-day DSF was greatest in the FTDM (accounting for 77.14% of monthly occurrences) in the WR, MTDM (45.85%) in the MR, and LTDM (72.12%) in the ER, showing a clear trend of movement from the WR to the ER. Temporal analysis of DSF revealed trend changes over time at annual and 10-day scales, with mutation points at 1985 and 2000. We applied single-factor and multiple-factor analyses to explore the driving mechanisms of DSF at the 10-day scale. Among single factors, a low wind-speed threshold, high solar radiation, and high evaporation were correlated with a high DSF, effectively explaining the variations in DSF at the 10-day scale; however, temperature, relative humidity, and precipitation poorly explained variations in DSF. Similarly, multiple-factor analysis using a classification and regression tree revealed that maximum wind speed was a major influencing factor of dust storm occurrence at the 10-day scale, followed by relative humidity, evaporation, and solar radiation; temperature and precipitation had weak influences. These findings help clarify the mechanisms of dust storm occurrence in East Asia.