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.

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.

The wide adoption of combined heat and power (CHP) can not only improve energy efficiency, but also strengthens energy system resiliency. While CHP reduces overall emissions compared to generating the same amount of electricity and heat separately, its on-site nature also means that CHP facilities operate in populated areas, raising concerns over their near-source air quality impact. Evaluation of the near-source impact of distributed CHP is limited by emission data availability, especially in terms of particulate matter (PM). In this paper, we report on stack emission testing results of a community-scale CHP plant with two natural gas turbine units (15 MW each) from measurements conducted in both 2010 and 2015, and assess the near-source air quality impact using an integrated modeling framework using the stack test results, site-specific meteorological data and terrain profiles with buildings. The NOx removal efficiency by selective catalytic reduction (SCR) is estimated to be ∼83% according to the emission testing. The integrated framework employs AERMOD to screen air quality in a 2.7 km × 2.3 km domain from 2011 to 2015 to identify the highest ground-level concentrations (GLCs). Examining the corresponding meteorological conditions, we find that those high GLCs appeared during the stable atmospheric boundary layer with relative high wind speed. Next, the worse-case scenarios identified from the screening process are simulated using the detailed Unsteady Reynolds Averaged Navier-Stokes (URANS) model coupled with a chemistry solver. The results generally show low GLCs of primary PM₂.₅ for this case study. However, our analysis also suggests greater building downwash impacts with the presence of taller and denser urban structures. Therefore, the near-source impact of natural gas-fired CHP in large metropolitan areas is worthy of further investigation.

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.

The association between ambient temperature and mortality has been well documented worldwide. However, limited data are available on nonfatal health outcomes, such as emergency department visits (EDVs), particularly from China.To examine the temperature-EDV association in 12 Chinese cities; and to assess the modification effects by region, gender and age.Daily meteorological data and non-accidental EDVs were collected during 2011–2014. Poisson regression with distributed lag non-linear model was applied to examine the temperature-lag-EDV association in each city. The effect estimates were pooled using multivariate meta-analysis at the national and regional level. Stratified analyses were performed by gender and age-groups. Sensitivity analyses adjusting for air pollution and relative humidity were conducted.A total of 4,443,127 EDVs were collected from the 12 cities. Both cold and hot temperatures were associated with increased risk of EDVs, with minimum-mortality temperature located at 64th percentile of temperature. The effect of cold temperature appeared on day 2 and persisted until day 30, causing a cumulative relative risk (RR) of 1.80 (1.54, 2.11). The effect of hot temperature appeared immediately and lasted until day 3, with a cumulative RR of 1.15 (1.03, 1.29). The effect of temperature on EDVs was similar in male and female but was attenuated with increasing age. The effect of cold temperature on EDVs was greater in southern areas of the country whereas the hot effect was greater in northern cities. The association was robust to a large range of sensitivity analyses.In China, there is a U-shaped association between temperature and risk of EDVs that is independent of air pollution and humidity. The temperature-EDV association varies with latitude and age-groups but is not affected by gender. Forecasting models for hospital emergency departments may be improved if temperature is included as an independent predictor.

Canopy-level stomatal conductance over a warm-temperate mixed deciduous and evergreen broadleaf forest in Japan was estimated by the Penman–Monteith approach, as compensated by a semi-empirical photosynthesis-dependent stomatal model, where photosynthesis, relative humidity, and CO2 concentration were assumed to regulate stomatal conductance. This approach, using eddy covariance data and routine meteorological observations at a flux tower site, permits the continuous estimation of canopy-level O3 uptake, even when the Penman–Monteith approach is unavailable (i.e. in case of direct evaporation from soil or wet leaves). Distortion was observed between the AOT40 exposure index and O3 uptake through stomata, as AOT40 peaked in April, but with O3 uptake occurring in July. Thus, leaf pre-maturation in the predominant deciduous broadleaf tree species (Quercus serrata) might suppress O3 uptake in springtime, even when the highest O3 concentrations were observed.

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.

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.

Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum of means over 35 ppb) in their health impact assessments. Is this paper, the artificial neural network (ANN) approach was used to forecast SOMO35 at the national level for a set of 24 European countries, mostly EU members. Available ozone precursors’ emissions, population and climate data for the period 2003–2013 were used as inputs. Trend analysis had been performed using the linear regression of SOMO35 over time, and it has demonstrated that majority of the studied countries have a decreasing trend of SOMO35 values.The created models have made majority of predictions (≈60%) with satisfactory accuracy (relative error <20%) on testing, while the best performing model had R² = 0.87 and overall relative error of 33.6%. The domain of applicability of the created models was analyzed using slope/mean ratio derivate from the trend analysis, which was successful in distinguishing countries with high from countries with low prediction errors. The overall relative error was reduced to <14%, after the pool of countries was reduced based on the abovementioned criterion.