Atmospheric Pollution Control Research Articles

Effect of Evaluation of Various Emission Control Policies on PM2.5 Reduction.

In previous work, a methodology was developed to discuss the influence of meteorological factors, policies, and surrounding cities on PM2.5 concentrations in a city. Two models were constructed using Zibo City, Shandong Province, as the target city. Initially, we improved the established PM2.5-Meteorological-Policy (PMP) model and applied it to six other target cities in Shandong Province. Concurrently, a novel model named the PM2.5-Interregional (PI) model was further developed in each city to directly express the influence of surrounding cities on the target cities. The model construction period was from January 2014 to August 2022, with the extended prediction period until November 2022. The results confirmed that disparities in the spatial distribution in seasons became smaller after the implementation of environmental policies. Moreover, two models in each city revealed good interpretation with high adjusted R2 values (>0.7) and lower MAPE and RMSE values (the lowest was 5.53% and 2.57), suggesting reasonable short-term prediction. Additionally, meteorological factors and the combined implementation of different policy types played crucial roles in reducing PM2.5 concentrations in all cities. Specifically, the temperature and wind speed were negatively correlated with PM2.5 concentrations in all models, with temperature having a stronger influence. The Law of the People's Republic of China on the Prevention and Control of Atmospheric Pollution (LAPAP), implemented in 2016, had a clear influence on reducing PM2.5 concentrations, with the highest absolute fitted coefficient in most cities (-0.166 to -0.344). On the contrary, the influence of temperature seemed to be more significant compared to policies, due to the larger standardized coefficient in each city (-0.606 to -0.864).

Atmospheric Nitrogen Pollution Control Benefits the Coastal Environment.

Nitrogen is an essential nutrient and a major limiting element for the ocean ecosystem. Since the preindustrial era, substantial amounts of nitrogen from terrestrial sources have entered the ocean via rivers, groundwater, and atmospheric deposition. China serves as a key hub in the global nitrogen cycle, but the pathways, sources, and potential mitigation strategies for land-ocean nitrogen transport are unclear. By combining the CHANS, WRF-Chem, and WNF models, we estimated that 8 million tonnes (Tg) of nitrogen was transferred into the ocean in 2017 in China, with atmospheric deposition contributing 1/3. About half variation of the offshore chlorophyll concentration was explained by atmospheric deposition. The Bohai Sea was the hot spot of nitrogen input, estimated at 214 kg N ha-1, while other areas were around 25-51 kg N ha-1. The largest contributors are agricultural systems (4 Tg, 55%), followed by domestic sewage (2 Tg, 21%). Abatement measures could reduce nitrogen export to the ocean by 43%, and mitigating ammonia and nitrogen oxide emissions accounts for 33% of this reduction, highlighting the importance of addressing air pollution in resolving ocean pollution. The cost-benefit analysis suggests the priority of nitrogen reduction in cropland and transport systems for the ocean environment.

Adsorption of NOx by graphene oxide loaded with CeO2 as a catalyst for atmospheric pollution control

In this study, we utilized graphene oxide (GO) loaded with cerium oxide (CeO2) to synthesize GO-CeO2 catalysts using a hydrothermal method and high-temperature calcination. The performance of the catalyst was evaluated by characterization of the catalyst material and testing for nitrogen oxide (NOx) conversion rates and nitrogen gas adsorption isotherm in mixed flue gas. The results demonstrated that increasing the ratio of NO2 in the NO+NO2 mixture significantly enhanced the conversion rate of NOx. Notably, when the proportion of NO2 reached 0.4, the denitrified gas displayed a remarkable increase in NOx conversion rate, exceeding 98%. This finding highlighted that even low concentrations of NO2 can accelerate denitrification reactions. Moreover, increasing the oxygen concentration in the gas exhibited considerable potential to elevate the NOx conversion rate. The oxygen concentration played a vital role in the process of denitrification, and the GO-CeO2 catalyst exhibited a high oxygen storage capacity. Moreover, the GO-CeO2 catalyst maintained a high denitrification efficiency even under high-temperature conditions and demonstrated excellent NOx conversion performance. The catalyst also demonstrated good stability and strong water resistance under varying proportions and temperatures while maintaining a consistent denitrification efficiency. These findings indicated the significant adsorption effect of the graphene oxide loaded with CeO2 catalyst on NOx, suggesting promising potential for application and widespread use.

Low-temperature sintering of multifunctional SiC catalytic membrane for synergistic emission control of toluene and dust

SiC catalytic membranes have shown great potential in dust and atmospheric pollutant emission control. However, their high sintering temperature (up to 1850 °C) and the ‘trade-off’ effect between its gas permeance and catalytic performance have hindered their widespread application. In this study, a high-performance CuO-loaded SiC catalytic membrane was fabricated to address the above issues using a liquid phase sintering and surficial reconstruction (LSSR) strategy. The low melting point of CuO enables the liquid phase sintering of CuO-SiC membrane at a significantly reduced temperature of 1040 °C, and the formation of CuO nanoparticles (CuO-NPs) on bulk CuO in CuO-SiC membrane (CuO-NPs/CuO-SiC) is achieved through a surficial reconstruction process. The resulting CuO-NPs/CuO-SiC catalytic membrane exhibits a high gas permeance of 275.8 m3·m−2·h−1·kPa−1 and a bending strength of 14.2 MPa. The CuO-NPs/CuO-SiC catalytic membrane also displays complete (100 %) toluene oxidation and PM2.5 filtration efficiencies at 280 °C, as well as enhanced water vapor resistance and long-term stability. The LSSR was successfully applied to fabricate an CuO-NPs/CuO-Al2O3 catalytic membrane, which shows complete toluene oxidation and PM2.5 filtration efficiency at 300 °C, demonstrating that LSSR is readily extended to fabricating other ceramic and metal-ceramic-based catalytic membranes.

The Spatiotemporal Distribution of NO2 in China Based on Refined 2DCNN-LSTM Model Retrieval and Factor Interpretability Analysis

With the advancement of urbanization in China, effective control of pollutant emissions and air quality have become important goals in current environmental management. Nitrogen dioxide (NO2), as a precursor of tropospheric ozone and fine particulate matter, plays a significant role in atmospheric chemistry research and air pollution control. However, the uneven ground monitoring stations and low temporal resolution of polar-orbiting satellites set challenges for accurately assessing near-surface NO2 concentrations. To address this issue, a spatiotemporal refined NO2 retrieval model was established for China using the geostationary satellite Himawari-8. The spatiotemporal characteristics of NO2 were analyzed and its contribution factors were explored. Firstly, seven Himawari-8 channels sensitive to NO2 were selected by using the forward feature selection based on information entropy. Subsequently, a 2DCNN-LSTM network model was constructed, incorporating the selected channels and meteorological variables as retrieval factors to estimate hourly NO2 in China from March 2018 to February 2020 (with a resolution of 0.05°, per hour). The performance evaluation demonstrates that the full-channel 2DCNN-LSTM model has good fitting capability and robustness (R2 = 0.74, RMSE = 10.93), and further improvements were achieved after channel selection (R2 = 0.87, RMSE = 6.84). The 10-fold cross-validation results indicate that the R2 between retrieval and measured values was above 0.85, the MAE was within 5.60, and the RMSE iwas within 7.90. R2 varied between 0.85 and 0.90, showing better validation at mid-day (R2 = 0.89) and in spring and fall transition seasons (R2 = 0.88 and R2 = 0.90). To investigate the cooperative effect of meteorological factors and other air pollutants on NO2, statistical methods (beta coefficients) were used to test the factor interpretability. Meteorological factors as well as other pollutants were analyzed. From a statistical perspective, PM2.5, boundary layer height, and O3 were found to have the largest impacts on near-surface NO2 concentrations, with each standard deviation change in these factors leading to 0.28, 0.24, and 0.23 in standard deviations of near-surface NO2, respectively. The findings of this study contribute to a comprehensive understanding of the spatiotemporal distribution of NO2 and provide a scientific basis for formulating targeted air pollution policies.

Detection of PM2.5 spatiotemporal patterns and driving factors in urban agglomerations in China

As important urban gathering places for new urbanization construction, urban agglomerations (UAs) have long suffered from high concentrations of PM2.5 pollution. By analyzing the changes of the temporal and spatial distribution patterns and the natural and human drivers of PM2.5 in UAs in China can provide an effective reference basis for atmospheric pollution prevention and control. In this study, based on PM2.5 concentration data from 2000 to 2020, PM2.5 pollution in 19 UAs in China was investigated. First, the annual average PM2.5 concentration was used to analyze the temporal variation, spatial distribution, and agglomeration characteristics of PM2.5 pollution in each UA. Then, factor detection was used to analyze the driving mechanisms of the effects of natural and human factors on PM2.5 pollution in UAs in China. Finally, the explanatory intensities of the interactions between different factors of PM2.5 pollution were analyzed based on interaction detection. The results show that from 2000 to 2020, the PM2.5 concentration exhibited an evolutionary trend of “M”-shape. An inflection point occurred in 2007, and 2020 had the lowest value for the study period. PM2.5 of high concentration took the Central Plains and the Beijing-Tianjin-Hebei UAs as the core of pollution. 2013 was an important turning point with a spatial pattern of expansion to the surrounding areas and then contraction to the center. UAs presented an elliptical spatial distribution, and outliers occurred in individual years. Humidity and temperature were the dominant natural factors for the variation of the PM2.5 concentration in 2013, 2018, and 2020, and their q-values were 0.276(humidity), 0.429(temperature), and 0.375(temperature), respectively. The total population and energy consumption were the dominant human factors for these three years, with q-values of 0.150(total population), 0.259(total population), and 0.196(energy consumption), respectively. The explanatory effect of the two-factor interaction was higher than that of a singular factor. Furthermore, the factor and interaction detection of natural factors found them to be more significant driving mechanisms than human factors. The results of this study not only allow for the understanding of the spatiotemporal variation of the PM2.5 concentration in UAs and the effects of different influencing factors, but also provide a scientific reference for the steady development of UAs and air pollution prevention and management in China.

High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data

This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.

Spatial prediction of PM[formula omitted] concentration using hyper-parameter optimization XGBoost model in China

High-fine particulate matter (PM2.5) pollution has become the main object of damaging the atmospheric environment and endangering human health. Accurate prediction of the spatial variability of PM2.5 concentrations using high-performance models is crucial for the prevention and control of atmospheric pollution. In this paper, in order to avoid the uncertainty caused by artificially setting the parameters of the prediction model, a new hyper-parameter optimization extreme gradient boosting (HPO-XGBoost) model for spatial prediction of PM2.5 concentration was proposed. We used aerosol optical depth (AOD) data of Himawari-8 satellite combined with the HPO-XGBoost model to predict and mapping the spatial variability of PM2.5 concentrations during the COVID-19 lockdown in China. The results showed that the maximum concentration of PM2.5 exhibited a downward trend and a significant seasonal fluctuation from 2015 to 2020. Compared with grid search (GS) and random grid search (RGS) optimization algorithms, the tree-structured Parzen estimator-XGBoost (TPE-XGBoost) approach has the highest prediction accuracy (R2) of 89.37% (January), 85.58% (February), 80.02% (March), and 83.68% (April) for PM2.5 concentrations in 2020, respectively. Spatial mapping of the monthly average PM2.5 concentration from January to April using the TPE-XGBoost model showed that the regions with higher PM2.5 concentrations are concentrated in northern China, eastern China and northeastern China, where PM2.5 concentrations exceed 150 μg m−3 in January. Furthermore, the spatial correlation analysis showed that factors with the highest driving effect of PM2.5 concentrations during the COVID-19 lockdown were factory (FAC) and traffic (TRA), and the strongest contribution of the double-factor synergy (q > 0.8) for all four months included factory-residence (FAC-RES) and factory-population (FAC-POP). The contribution source of PM2.5 transport showed a long-distance channel in Taiyuan, Xi’an and Shijiazhuang, while the cluster-like distribution and mixed distribution of contribution sources were embodied in Zhengzhou, Changchun and Harbin based on concentration-weighted trajectory (CWT) analysis. The above results showed that the TPE-XGBoost has strong nonlinear explanation and effective feasibility for spatial prediction of PM2.5 concentrations, and the transport of PM2.5 was dynamic effect which should be considered into account when developing prevention and control measures of atmospheric pollution in epidemic period.

Revealing Driving Factors of Urban O3 Based on Explainable Machine Learning

Driven by precursor emissions, meteorological conditions, and other factors, atmospheric ozone (O3) has become the main pollutant affecting urban air quality in summer. The current deductive models driven by physical and chemical mechanisms require a large number of parameters for the analysis of O3 pollution, and the calculation timeliness is poor. The data-driven inductive models are efficient but have problems such as poor explanation. In this study, an explainable model of data-driven Correlation-ML-SHAP was established to reveal the strongly correlated influencing factors of O3 concentration. Additionally, the machine learning ML module coupled with the explainable SHAP module was used to calculate the contributions of driving factors to O3 concentration, so as to realize the quantitative analysis of driving factors. The O3 pollution process in the summer of 2021 in Jincheng City was used as an example to carry out the application research. The results showed that the Correlation-ML-SHAP model could reveal and use strong driving factors to simulate O3 concentration and quantify influence contribution, and the ML module used the XGBoost model to achieve the best simulation accuracy. Air temperature, solar radiation, relative humidity, and precursor emission level were the strong driving factors of O3 pollution in Jincheng City in summer 2021, and the contribution weights were 32.1%, 21.3%, 16.5%, and 15.6%. The contribution weights of air temperature, solar radiation, and precursor emission level increased by 3.4%, 1.2%, and 1.2% on polluted days, respectively, and the contribution weights of precursor emission level rose to third place on polluted days. Each driving factor had a nonlinear interaction effect on O3 concentration. When the air temperature exceeded 24℃, or the relative humidity was lower than 70%, there was a 94.9% and 94.1% probability of positive contribution to O3 pollution, respectively. Under such meteorological conditions, ρ(NO2) exceeded 9 μg·m-3, or ρ(CO) exceeded 0.7 mg·m-3, and there was a 94.9% and 99.3% probability of positive contribution to O3 pollution, respectively. The southeast wind speed was lower than 5.8 m·s-1, or the south wind speed was lower than 5.3 m·s-1, both of which contributed positively to O3 pollution. The model quantitatively analyzed the influence contribution of various driving factors on urban O3 concentration, which could provide a basis for the prevention and control of urban atmospheric O3 pollution in summer.

Accelerated recovery of lake macroinvertebrates in the third decade since the reversal of acidification

Chemical reversal from acidification has been progressing in European freshwaters since the late 1980s, responding to successful control of atmospheric pollution by acidifying emissions. However, biological recovery is often delayed after improvements in water composition. We studied macroinvertebrate recovery from acidification in eight glacial lakes in the Bohemian Forest (central Europe) between 1999 and 2019. The chemical composition of these lakes reflects a complex of environmental changes, dominated by a very steep decline in acid deposition and, currently, by elevated nutrient leaching following climate-induced tree dieback within their catchments. Temporal trends in species richness, abundance, species traits and community composition were evaluated with regard to water chemistry, littoral habitat properties and fish colonisation. The results showed accelerated recovery of macroinvertebrates following two decades of gradual improvement in water composition and slowly progressing biological rehabilitation. We observed a significant increase in macroinvertebrate species richness and abundance, coupled with distinct changes in community composition, the extent of changes varying between lakes, reflecting different littoral habitat properties (vegetated vs. stony) and water chemistry. Overall, the communities shifted toward more specialised (grazers, filterers, and phytophilous species) and acid-tolerant taxa at the expense of detritivorous, eurytopic and acid-resistant taxa. Where fish reappeared, open-water taxa declined greatly. Compositional changes were likely driven by the combined effects of water chemistry reversal, rehabilitation of habitat conditions and fish colonisation. Despite favourable trends, communities in recovering lakes still lack several biotic elements, particularly less vagile, acid-sensitive taxa and specialised herbivores known from the regional species pool. It is expected that future progress in lake recovery will be further promoted or inhibited by stochastic colonisation or disturbance events.

Temporal variations of urban re-suspended road dust characteristics and its vital contributions to airborne PM2.5/PM10 during a long period in Beijing

Re-suspended road dust RRD as RRD2.5 and RRD10 can even more easily enter the atmospheric environment, showing a kind of significant potential to influence atmospheric environment. A campaign of sampling RRD samples at 53 sites and aerosol samples at a representative urban site in Beijing in October 2014, January, April and July 2015 was accomplished, and combined with RRD in 2003, and 2016–2018 periods to investigate the seasonal variations of chemical components in RRD2.5 and RRD10, long-term evolutions of RRD characteristics in 2003–2018, and source composition changes of RRD. Meanwhile a technique based on Mg/Al indicator for effectively estimating contributions of RRD to PM was developed. It is found that pollution elements and water-soluble ions in RRD were largely enriched in RRD2.5. The pollution elements presented an obvious seasonal variation in RRD2.5, however showed various seasonal variations in RRD10. These pollution elements in RRD, due to being mainly impacted by both increasing traffic activities and atmospheric pollution control measures, almost display a single-peak change in 2003–2018. The water-soluble ions in RRD2.5 and RRD10 presented various seasonal variations, and displayed an evident increase in 2003–2015. The source composition of RRD in 2003–2015 posed a significant change that traffic activities, crustal soil, secondary pollution species and biomass combustion became significant contributors to RRD. The contributions of RRD2.5/RRD10 to mineral aerosols in PM2.5/PM10 presented a similar seasonal variation. The synergistic effects of meteorological factors and anthropogenic activities in different seasons were significant motive force influencing the contributions of RRD to the mineral aerosols. The pollution elements Cr and Ni in RRD2.5 were the significant contributors to PM2.5, however, Cr, Ni, Cu, Zn, and Pb in RRD10 were the important contributors to PM10. The research will provide a new significant scientific guide for further controlling atmospheric pollution and improving air quality.

Photooxidation potential of fine particles from desulfurization flue gas aerosol

In China, desulfurization flue gas from coal-fired power plants (CFPPs) has been the focus of research for many years. In this study, for the first time, we simulated the emission process of flue gas and prepared photocatalysts by deposition. An innovative photocatalytic perspective was applied to study the composition, photocatalytic properties, hygroscopicity, and redox properties of the fine particles from flue gas aerosols. The characterization results of XRD, HRTEM, and XPS of freeze-dried desulfurized flue gas samples indicated that aerosol condensation occurred during the smoke plume transportation process, resulting in the formation of well-crystallized fine particles primarily consisting of soluble salts and metal oxides. The UV–vis and ESR results confirmed that desulfurized flue gas has UV light utilization and photocatalytic potential for producing oxidation groups such as hydroxyl radicals and superoxide radicals in light. The subsequent photocatalyst experiments revealed an 85.4 % reduction of 7 ppm isoprene in 30 min; ozone production (373 μg/m3) was observed under more complicated conditions (NO2 = 80 ppb, VOCs = 7 ppm). As a result of the study, a new research approach was proposed for exploring the photoactive potential of flue gas aerosol produced by CFPPs, as well as its impact on the concentration of VOCs and ozone. The finding provided a new possible pathway for desulfurization flue gas emissions and a reference for futureresearch on the observation and control of atmospheric pollution.

Tailoring the Composition of BaxBO3 (B = Fe, Mn) Mixed Oxides as CO or Soot Oxidation Catalysts in Simulated GDI Engine Exhaust Conditions.

Mixed oxides with perovskite-type structure (ABO3) are promising catalysts for atmospheric pollution control due to their interesting and tunable physicochemical properties. In this work, two series of BaxMnO3 and BaxFeO3 (x = 1 and 0.7) catalysts were synthesized using the sol-gel method adapted to aqueous medium. The samples were characterized by μ-XRF, XRD, FT-IR, XPS, H2-TPR, and O2-TPD. The catalytic activity for CO and GDI soot oxidation was determined by temperature-programmed reaction experiments (CO-TPR and soot-TPR, respectively). The results reveal that a decrease in the Ba content improved the catalytic performance of both catalysts, as B0.7M-E is more active than BM-E for CO oxidation, and B0.7F-E presents higher activity than BF for soot conversion in simulated GDI engine exhaust conditions. Manganese-based perovskites (BM-E and B0.7M-E) achieve better catalytic performance than iron-based perovskite (BF) for CO oxidation reaction due to the higher generation of actives sites.

Full-coverage spatiotemporal estimation of surface ozone over China based on a high-efficiency deep learning model

Ozone concentration Monitoring is essential to atmospheric pollution prevention and control. Against the background of severe ozone pollution over China in recent years, a spatiotemporal contiguous ozone concentration mapping method was developed. We imputed the significant data gaps of the Ozone Monitoring Instrument's tropospheric NO2 content by using an efficient machine learning model named LightGBM. Then, we developed a deep learning model based on three-dimensional Convolutional Neural Network architecture for daily maximum 8 h average ozone concentration estimation over China. With the support of the satellite-retrieved precursor, meteorological data and other ancillary data, our model achieved excellent performance with sample-based 10-fold cross-validation R2 = 0.88. Furthermore, we generated daily maximum 8 h average ozone datasets covering the whole of China from 2016 to 2020. This study presents a novel method for surface ozone modeling, which can provide fundamental data for the study of the ecological changes caused by ozone pollution, such as crop loss, or the harmful effects on humans, such as the increased incidence of respiratory diseases.

The optimization of XGBoost model and its application in PM2.5 concentrations estimation based on MODIS data in the Guanzhong region, China

ABSTRACT Atmospheric pollution affects air quality and can pose a serious risk to public health. Traditional PM2.5 monitoring is limited by the uneven distribution of ground stations, which makes it difficult to obtain spatially continuous and accurate PM2.5 concentrations information. Using aerosol optical depth (AOD) retrieved from satellite remote sensing to study the temporal and spatial variation characteristics of PM2.5 can accurately predict the PM2.5 concentrations in a wide range and provide a basis for atmospheric pollution prevention and control. Considering the spatio-temporal correlations among the data, this study uses AOD data, introduces geographic location data, temporal data, meteorological data, and elevation data to construct an Optimized XGBoost (O-XGBoost) model with spatial and temporal characteristics to estimate PM2.5 concentrations in the Guanzhong region from 2019 to 2021 and analyse its spatial and temporal distribution characteristics. The results show that compared with the random forest (RF) and XGBoost models, the O-XGBoost model has higher estimation accuracy, with , , and of 0.873, 11.460, and 8.061, respectively. The estimation of PM2.5 concentrations based on the O-XGBoost model makes up for the uneven distribution of ground monitoring stations and improves the estimation accuracy.

Application of data mining combined with power data in assessment and prevention of regional atmospheric pollution

The continuous development of society has destroyed the living environment of human beings to a large extent. The problem of air pollution is becoming more and more serious, which affects people’s daily life and threatens human health and the natural environment. It is very important to evaluate and prevent air pollution. The existing atmospheric environmental pollution assessment system has problems such as low accuracy of prediction results and incomplete analysis of problems, resulting in untimely prevention and control of regional atmospheric environmental pollution. This paper analyzes the needs of atmospheric environment data mining, monitoring the prevention and control of atmospheric environment pollution, using data mining technology combined with power data to evaluate and prevent air pollution. Through the real-time detection and evaluation of the regional environmental pollution index, it is possible to carry out timely treatment when the atmospheric environmental pollution does not meet the standard. Through the prediction accuracy test, the environmental pollution index test, the atmospheric environment quality score test and the prevention and control effect test in different regions, it is found that the data mining technology is used for prevention and control. The prediction accuracy increased by 4.18%, and the environmental pollution index decreased. It is more conducive to the control of regional atmospheric pollution, the rate of atmospheric environmental quality score has been increased by 2.38%, and the control effect has been optimized by 3.1%. Data mining technology is more conducive to the assessment and prevention of regional air pollution.

Characteristics of volatile organic compounds in 11 cities along Taihang Mountain, an industrial region in Northern China

The cities along Taihang Mountain, one of the most important industrial regions in China, have faced challenges in atmospheric pollution control due to the development of industries. However, few studies have focused on the pollution characteristics of volatile organic compounds (VOCs) in this region. The concentrations, composition, sources, and chemical reactivity of VOCs were analyzed in 11 cities along Taihang Mountain. The average VOC concentration in the 11 cities (31.4 ± 11.3 ppbv) was higher than that for most cities in China, which was attributed to emissions from coal combustion and industrial processes. Four cities in Hebei province had the highest VOC concentration (32.9 ± 10.9 ppbv). Alkene (7.4 ± 3.9 ppbv) and alkyne (2.6 ± 1.9 ppbv) concentrations in the 11 cities were higher than those in other Chinese cities (2.4–5.9 and 1.0–3.8 ppbv, respectively). Shanxi Province had the largest contributions of alkenes (23.6%) and alkynes (9.7%). Based on the benzene/toluene (B/T) ratio, the 11 cities were divided into three categories: four had B/T ratios of 0.5–0.6, and were classified the “traffic” cities, and four with B/T ratios of 1.5–2.2 were classified as “coal combustion” cities. The chemical reactivity results showed that alkenes were the most important VOCs in terms of ozone formation. Coal combustion and industrial processes were the main emission sources affecting VOCs and O3 pollution in the 11 cities along Taihang Mountain. VOC researchers should pay more attention to this region, and more refined research should be conducted.

Spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors in the Niger River Watershed, West Africa

Identifying the main factors influencing PM2.5 concentrations is critical for the control and management of elevated atmospheric pollution. Although the impact of natural and land use factors on PM2.5 concentration has previously been quantified, in this paper we show that multiple environmental factors exhibit significant spatial heterogeneity at a range of scales. A more comprehensive analysis of PM2.5 variations require the potential effects of seasonal variations in meteorological conditions, interactions between natural and land use factors, and possible multi-scale spatial effects to be accounted for. Based on the global remote sensing inversion dataset, the aim of this study is to elucidate the spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors, and their multi–scale effects. This is achieved through combining geographical detector (GD), random forest (RF) and multiscale geographically weighted regression modelling (MGWR) to reveal the underlying mechanisms of spatial variation of PM2.5 concentrations in the Niger River Watershed (NRW). The results show that natural factors, represented by meteorological and topography variables, are most prominent. Human activities, represented by land use, have significant indirect effects through interactions with meteorological factors. Elevation (ELE) and annual minimum temperature (TMIN) emerge as the most important interaction centers among natural factors. In addition, the interaction between natural and land use factors are crucial in considering PM2.5 transmission and dispersion in the NRW. The topography and meteorological factors underlie the basic spatial pattern PM2.5 concentrations in the NRW, among which seasonal meteorological parameters have a more prominent impact on PM2.5 concentration. Both natural and land use factors exhibit strong non–stationarity in respect of spatial variations in PM2.5, and the effect of environmental factors in general on PM2.5 is scale–dependent. Transport and dispersion are important constraints in terms of aerosol loading regionally. The study suggests that coupling processes between meteorological conditions, land use and PM2.5 concentrations, based on the scale effects of each factor, should be accounted for in developing aerosol pollution regulations and cooperative management strategies in the region.

RETRACTED: Mixed convective heat transfer in a power-law fluid in a square enclosure: Higher order finite element solutions

Incorporation of momentum gradients produced due to inertial motion of the lid along with the presence of temperature differences in the configuration make the physical problem more significant. The joint variation of momentum and thermal diffusion in diversified natural liquids is recognized as mixed convection. Valuable attention has been received by such a phenomenon in different areas of science and technology such as in wind current–based solar receivers, electronic instruments, control of emergency shutdown in reactors, thermal exchangers, oceanic currents, control of atmospheric pollution, and so on. So, the main focus is to contemplate hydrothermal characteristics of a power-law fluid contained in a square cavity with the movement of the upper lid and being thermally adiabatic. The other extremities are considered to be at rest, and the base wall is prescribed with uniform/non-uniform temperature distributions. The governing formulation of the problem is handled by executing a finite element approach. Hybrid meshing is performed for domain discretization, and weak variational formulation is utilized for formulation discretization. Second-degree polynomials are employed as the interpolation function, providing information about velocity and temperature distributions at boundary and intermediate nodes. The system of finalized non-linear equations is resolved by using the Paradiso software. The results for velocity and temperature distributions are attained comparatively for uniformly and non-uniformly heated profiles. The kinetic energy and average Nusselt number are also computed against flow concerning variables. From the attained graphical and tabular data, it is deduced that by increasing the Reynolds number, inertial forces dominate over buoyancy forces and the effect of lid movement is prominent on flow characteristics. It is also inferred that for the shear thickening case and for all values of the Reynolds number, the average Nusselt number shows a constant behavior.

Study on the control targets and measures for total diesel consumption from mobile sources in Beijing, China

Based on the current air pollution control and CO2 emission reduction policies, this study analyzed the energy structure, number of motor vehicles and nonroad mobile machinery, energy consumption and pollutant emissions in Beijing. Furthermore, the diesel consumption characteristics and challenges for emission reduction in key fields were investigated, such as medium- and heavy-duty diesel trucks, long-distance passenger and tourist diesel vehicles, and nonroad machinery, which are areas with difficult-to-reduce diesel consumption. Control targets and measures for total diesel consumption were also proposed. The results indicated that the higher diesel consumption per unit area in Beijing is related to the larger passenger car and freight truck populations. In recent decades, the number of diesel vehicles has increased, the vehicle type structure has been optimized, the proportion of vehicles with high emission standards has increased, and the absolute pollutant emissions have decreased. Among these, nitrogen oxides (NOx), fine particulate matter (PM2.5) and volatile organic compound (VOC) emissions of different models decreased by 39.5%, 75.3% and 42.8%, respectively, while carbon dioxide (CO2) emissions from diesel combustion decreased by 32%. Moreover, medium and large passenger vehicles, medium- and heavy-duty trucks and construction machinery are the main contributors to diesel consumption. These vehicle types are also difficult to control and reduce, and their replacement by new-energy vehicles is relatively limited. The main control measures for diesel consumption are as follows. First, a green transportation mode can be adopted for goods that can be converted from roads to railways. Second, fuel consumption reduction for nonroad mobile machinery can be realized by tightening fuel consumption limits, setting appropriate maximum retirement life, establishing low- or ultralow-emission zones, and establishing demonstration plots for electric vehicle (EV) substitution for mobile machinery. To improve the air quality and take the lead in carbon neutrality in the future, Beijing must further accelerate the energy structure adjustment and the development of new-energy vehicles in the transportation sector. Carbon neutralization is an important opportunity for diesel consumption reduction, and the synergistic control of atmospheric pollution and carbon emissions from diesel combustion must be strengthened.