Investigating the impacts of coal-fired power plants on ambient PM2.5 by a combination of a chemical transport model and receptor model

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 μg/m3 to 44.8416 μg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

Hourly forecasting on PM2.5 concentrations using a deep neural network with meteorology inputs

The PM2.5 (particulate matter with a diameter of fewer than 2.5 µm) has become a global topic in environmental science. The neural network that based on the non-linear regression algorithm, e.g., deep learning, is now believed to be one of the most facile and advanced approaches in PM2.5 concentration prediction. In this study, we proposed a PM2.5 predictor using deep learning as infrastructure and meteorological data as input, for predicting the next hour PM2.5 concentration in Beijing Aotizhongxin monitor point. We efficiently use the parameter's spatiotemporal correlation by concatenating the dataset with time series. The predicted PM2.5 concentration was based on meteorology changes over a period. Therefore, the accuracy would increase with the period growing. By extracting the intrinsic features between meteorological and PM2.5 concentration, a fast and accurate prediction was carried out. The R square score reached maximum of 0.98 and remained an average of 0.9295 in the whole test. The average bias of the model is 9 μg on the validation set and 1 μg on the training set. Moreover, the differences between the predictions and expectations can be further regarded as the estimation for the emission change. Such results can provide scientific advice to supervisory and policy workers.

The Joint Clean Air Actions and air quality spillovers in China

Facing severe air pollution in its North Plain, the central government of China initiated the Joint Clean Air Action (JCAA) in 2017 to facilitate pollution mitigation efforts across the region. While quite a few studies investigated the effectiveness of this regulation, much less attention is paid to the pollution spillover effects. We empirically examine the effects, and show that 1) air quality in the east of the target cities has been improved due to positive spillover of improved air quality under the JCAA; 2) the beneficiary spillover lasts for two seasons and disappeared in autumn and winter; 3) air quality in the north, south and west directions are almost not changed; 4) wind direction and topography, two determinants of atmospheric transport, have a considerable influence over the spillover effects. Our study provides a fresh perspective to understand the impacts of the JCAA policy and underlines the necessity of taking both pollution and air quality spillover effects into the cost-benefit analysis.Implications: Pollution regulations in one place may increase pollution in other places, as production and emissions are re-allocated under the incentives induced by regional-specific regulations. This phenomenon has long been recognized in the literature as pollution spillover. However, if the relevant production and emissions are not re-allocated, at least not re-allocated in large quantities, local air quality improvement induced by regulations may also benefit the neighboring areas. We call this effect air quality spillover. Both spillover effects should be rigorously evaluated, which is of scientific interest by itself and also contributes to a comprehensive cost-benefit analysis of environmental regulations.

Variations and drivers of aerosol vertical characterization after clean air policy in China based on 7-years consecutive observations

Understanding the aerosol vertical characterization is of great importance to both climate and atmospheric environment. This study investigated the variations of aerosol profiles over eight regions of interest in China after clean air policy (2013-2019) and discussed the drivers of the vertical aerosol structure, using observations from active satellite measurements (CALIPSO). From the annual variation, the amplitude of extinction coefficient profiles showed a decreasing trend with fluctuations, and the maximum was 0.21 km^-1 in Beijing-Tianjin-Hebei (JJJ). For regions suffered from air pollution, the variation was greatest below 0.45 km, while it was between 1-1.5 km for Sichuan Basin. The correlation coefficient between the relative humidity (RH) and the extinction coefficient indicated that the increase of RH inhibited the decrease of the extinction coefficient in the Yangtze River Delta. In most regions, the main aerosol subtypes were polluted dust and polluted continental, but they were coarser in JJJ and North West. The frequency of concurrency of dust and polluted dust aerosols decreased in JJJ, but polluted continental aerosols occurred more frequently. Further, the aerosol extinction coefficient profiles under different pollution conditions showed that it changed most during heavy pollution periods in JJJ, especially in 2017, with a significant aerosol loading between ∼700 and 1200 m. The atmospheric reanalysis data revealed that the weak convergence at low level and the divergence at high level supported the upward transport of aerosols in 2017. Overall, the differences in divergence allocation, RH, and wind filed were the main meteorological drivers.

Regional Transport of PM2.5 from Coal-Fired Power Plants in the Fenwei Plain, China

The Fenwei Plain (FWP) remains one of the worst PM2.5-polluted regions in China, although its air quality has improved in recent years. To evaluate the regional transport characteristics of PM2.5 emitted by coal-fired power plants in the FWP in wintertime, the primary PM2.5, SO2, and NOx emissions from coal-fired power plants with large units (≥300 MW) in 11 cities of the area in January 2019 were collected based on the Continuous Emission Monitoring System (CEMS). The spatial distribution and source contribution of primary and secondary PM2.5 concentrations were investigated using the Weather Research and Forecast (WRF) model and the California Puff (CALPUFF) model. The results showed that secondary PM2.5 was transported over a larger range than primary PM2.5 and that secondary nitrate was the main component of the total PM2.5 concentration, accounting for more than 70%. High concentrations of primary, secondary, and total PM2.5 mainly occurred in the Shaanxi region of the FWP, especially in Xianyang, where the PM2.5 concentrations were the highest among the 11 cities, even though its pollutant emissions were at moderate levels. The PM2.5 concentrations in Sanmenxia and Yuncheng primarily came from regional transport, accounting for 64% and 68%, respectively, while those in other cities were dominated by local emissions, accounting for more than 63%. The results may help to understand the regional transport characteristics of pollutants emitted from elevated point sources over a complex terrain.

Cross-boundary transport and source apportionment for PM2.5 in a typical industrial city in the Hebei Province, China: A modeling study

Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain. In this study, an industrial district (Shahe City) with a large glass manufacturing sector was investigated to clarify the relative contribution of fine particulate matter (PM_2.5) to the city's high levels of pollution. The Nest Air Quality Prediction Model System (NAQPMS), paired with Weather Research and Forecasting (WRF), was adopted and applied with a spatial resolution of 5 km. During the study period, the mean mass concentrations of PM_2.5, SO₂, and NO₂ were observed to be 132.0, 76.1, and 55.5 μg/m³, respectively. The model reproduced the variations in pollutant concentrations in Shahe at an acceptable level. The simulation of online source-tagging revealed that pollutants emitted within a 50-km radius of downtown Shahe contributed 63.4% of the city's total PM_2.5 concentration. This contribution increased to 73.9±21.2% when unfavorable meteorological conditions (high relative humidity, weak wind, and low planetary boundary layer height) were present; such conditions are more frequently associated with severe pollution (PM_2.5 ≥ 250 μg/m³). The contribution from Shahe was 52.3±21.6%. The source apportionment results showed that industry (47%), transportation (10%), power (17%), and residential (26%) sectors were the most important sources of PM_2.5 in Shahe. The glass factories (where chimney stack heights were normally < 70 m) in Shahe contributed 32.1% of the total PM_2.5 concentration in Shahe. With an increase in PM_2.5 concentration, the emissions from glass factories accumulated vertically and narrowed horizontally. At times when pollution levels were severe, the horizontally influenced area mainly covered Shahe. Furthermore, sensitivity tests indicated that reducing emissions by 20%, 40%, and 60% could lead to a decrease in the mass concentration of PM_2.5 of of 12.0%, 23.8%, and 35.5%, respectively.

Interaction of interregional O3 pollution using complex network analysis

In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O₃ in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This paper uses complex network theory to describe the network structure characteristics of O₃ pollution spatial correlation. On this basis, the node importance method is used to mine the sub-network with the highest spatial correlation in the O₃ network, and use transfer entropy theory to analyse the interaction of pollutants between regions. The results show that the O₃ area of Qilihe District, Lanzhou City can be divided into three parts: the urban street community type areas in urban areas, the township and village type areas in mountain areas and the scattered areas represented by isolated nodes. An analysis of the mutual influence of O₃ between each area revealed that the impact of O₃ on each monitoring station in adjacent areas will vary considerably. Therefore these areas cannot be governed as a whole, and the traditional extensive management measures based on administrative divisions cannot be used to replace all other regional governance measures. There is the need to develop a joint prevention and control mechanism tailored to local conditions in order to improve the accuracy and efficiency of O₃ governance.

Metal-Containing Nanoparticles in Low-Rank Coal-Derived Fly Ash from China: Characterization and Implications toward Human Lung Toxicity

Characterization of nanoparticles (NPs) in coal fly ashes (CFAs) is critical for better understanding the potential health-related risks resulting from coal combustion. Based on single-particle (SP)-inductively coupled plasma mass spectrometry (ICP-MS) coupled with transmission electron microscopy techniques, this study is the first to determine the concentrations and sizes of metal-containing NPs in low-rank coal-derived fly ashes. Despite only comprising a minor component of the studied CFAs by mass, NPs were the dominant fraction by particle number. Fe- and Ti-containing NPs were identified as the dominant NPs with their particle number concentration ranging from 2.5 × 10⁷ to 2.5 × 10⁸ particles/mg. In addition, the differences of Fe-/Ti-containing NPs in various CFAs were regulated by the coalification degree of feed coals and combustion conditions of all of the low-rank CFAs tested. In the cases where these NPs in CFAs become airborne and are inhaled, they can be taken up in pulmonary interstitial fluids. This study shows that in Gamble's solution (a lung fluid simulant), 51-87% of Fe and 63-89% of Ti (ratio of the mass of Fe-/Ti-containing NPs to the total mass of Fe/Ti) exist in the NP form and remain suspended in pulmonary fluid simulants. These NPs are bioavailable and may induce lung tissue damage.