The Driving Influence of Multi-Dimensional Urbanization on PM2.5 Concentrations in Africa: New Evidence from Multi-Source Remote Sensing Data, 2000-2018

Improvement of Surface PM2.5 Diurnal Variation Simulations in East Africa for the MAIA Satellite Mission

The Multi-Angle Imager for Aerosols (MAIA), supported by NASA and the Italian Space Agency, is planned for launch into space in 2025. As part of its mission goal, outputs from a chemical transport model, the Unified Inputs for Weather Research and Forecasting Model coupled with Chemistry (UI-WRF-Chem), will be used together with satellite data and surface observations for estimating surface PM2.5. Here, we develop a method to improve UI-WRF-Chem with surface observations at the U.S. embassy in Ethiopia, one of MAIA's primary target areas in east Africa. The method inversely models the diurnal profile and amount of anthropogenic aerosol and trace gas emissions. Low-cost PurpleAir sensor data are used for validation after applying calibration functions obtained from the collocated data at the embassy. With the emission updates in UI-WRF-Chem, independent validation for February 2022 at several different PurpleAir sites shows an increase in the linear correlation coefficients from 0.1-0.7 to 0.6-0.9 between observations and simulations of the diurnal variation of surface PM2.5. Furthermore, even by using the emissions optimized for February 2021, the UI-WRF-Chem forecast for March 2022 is also improved. Annual update of monthly emissions via inverse modeling has the potential and is needed to improve MAIA's estimate of surface PM2.5.

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021)

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

Effects of urbanisation on PM2.5 concentrations: A systematic review and meta-analysis

While urbanisation greatly improves a population's quality of life, it also has significant effects on urban air pollution. Previous studies have determined how urbanisation affects PM2.5 concentrations; the findings, however, have not been consistent. This study conducts a meta-analysis to systematically organise existing research and draw more conclusive and broadly applicable results regarding the impact of different factors of urbanisation on PM2.5 concentrations. The main research findings are as follows: (1) the Environmental Kuznets Curve (EKC) is proven to hold true in terms of the effect of population and land urbanisation on PM2.5 concentrations, while there is no consistent conclusion on the non-linear relationship between economic urbanisation and PM2.5 concentrations; (2) publication bias is evident in research on the economic and comprehensive urbanisation dimensions under linear assumptions; (3) there are notable heterogeneities in existing research in this field. The meta-regression model further indicates that model design, sample design, and publication characteristics might be responsible for these heterogeneities. This study innovatively applies a meta-analysis to investigate the effect of urbanisation on PM2.5 concentrations. The findings will contribute to scholars designing more rigorous research frameworks in this field.

Spatial and nonlinear effects of new-type urbanization and technological innovation on industrial carbon dioxide emission in the Yangtze River Delta

The purpose of this paper is to quantify the level of new-type urbanization and unravel the spatial and nonlinear effects of new-type urbanization and technological innovation on industrial carbon emissions. Although the impact of traditional urbanization levels on carbon emissions has been widely studied, there is still a huge room for optimization, and the impact of new-type urbanization on carbon emissions has not yet been clarified. Selecting 37 cities in the Yangtze River Delta as a research sample, this paper measures the new-type urbanization based on an evaluation system we build. Consequently, we assess the spatial and nonlinear effects of new-type urbanization and technological innovation on carbon emissions by the spatial Durbin model and non-parameter addictive model, respectively. The results indicate that the new-type urbanization and low-carbon city pilot policy have significant spatial spillover effects on reducing carbon dioxide emissions, while the economic growth plays a positive role in increasing carbon emission. As for nonlinear effects, there is a significant inverted "N"-shaped relationship between the level of new-type urbanization and carbon dioxide emissions, while the nexus between technological innovation and carbon emissions is an inverted "U"-shaped relationship. This paper provides a new perspective for confirming the mechanism of the new-type urbanization on carbon emissions. Meanwhile, these findings are of significance for the relevant authorities in China to develop appropriate policy in carbon dioxide emission reduction.

Multidimensional effects of urbanization on PM2.5 concentration in China

Recently, the contradiction between urbanization and the air environment has gradually attracted attention. However, most existing studies have explored the impact of single urbanization factors, such as population, the economy, or land, on PM_2.5 and ignored the impact of multidimensional urbanization on PM_2.5 concentration. Moreover, the heterogeneity in the mechanisms responsible for the PM_2.5 concentration caused by multidimensional urbanization has not been thoroughly studied in different regions in China. Therefore, we investigate the spatial-temporal evolution characteristics of PM_2.5 concentration in China during 1998-2019 by spatial analysis and dynamic panel models based on the environmental Kuznets curve (EKC). Then, we study the effects of multidimensional urbanization on PM_2.5 concentration, and analyze the dominant factors in China's eight economic regions. During the study period, the PM_2.5 concentration in China fluctuated before 2013 and gradually decreased thereafter. The PM_2.5 concentration has significant regional differences in China. Spatially, the PM_2.5 concentration is higher in the north than in the south and higher in the east than in the west. Additionally, there is a significant spatial spillover effect. Both population urbanization and economic urbanization show an inverted U-shaped relationship with PM_2.5 concentration in China, which is consistent with the classical EKC theory. Due to other socioeconomic factors, the PM_2.5 concentration tends to decrease linearly with increasing land urbanization rate. The effects of urbanization on the PM_2.5 concentration in the eight economic regions in China show significant differences. The effect of land urbanization on the PM_2.5 concentration is dominant in the Middle Yangtze River region, that of economic urbanization is dominant in northwestern China, and that of population urbanization is dominant in the remaining regions in China.

Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy

Air pollution has seriously hindered China’s sustainable development. The impact mechanism of industrial upgrading on air pollution is still unclear, given the rapid digital economy. It is necessary to analyze the impact of industrial structure upgrading on air pollution through the digital economy. To investigate the impact of industrial upgrading and the digital economy on air pollution, this paper selected the industrial advanced index and the digital economy index to construct a panel regression model to explore the improvement effect of industrial upgrading on air pollution and selected China’s three typical areas to construct a zonal regression model. The concentrations of air pollutants showed a downward trend during 2013–2020. Among them, the SO2 concentration decreased by 63%, which is lower than the PM2.5 and NO2 concentrations. The spatial pattern of air pollutants is heavier in the north than in the south and heavier in the east than in the west, with the North China Plain being the center of gravity. These air pollutants have significant spatial spillover effects, while local spatial correlation is dominated by high-high and low-low clustering. Industrial upgrading has a stronger suppressive effect on the PM2.5 concentration than the suppressive effect on the SO2 and NO2 concentrations, while the digital economy has a stronger improvement effect on the SO2 concentration than its improvement effect on the PM2.5 and NO2 concentrations. Industrial upgrading has a stronger improvement effect on air pollution in the Yangtze River Delta urban agglomeration than in Beijing–Tianjin–Hebei and its surrounding areas, while the improvement in air pollution attributable to the digital economy in Beijing–Tianjin–Hebei and its surrounding areas is stronger than in the Yangtze River Delta urban agglomeration. There are significant differences in the effects of industrial upgrading and the digital economy on the various types of air pollutants.

Land Use Dynamic Changes in an Arid Inland River Basin Based on Multi-Scenario Simulation

The Tarim River Basin is the largest inland river basin in China. It is located in an extremely arid region, where agriculture and animal husbandry are the main development industries. The recent rapid rise in population and land demand has intensified the competition for urban land use, making the water body ecosystem increasingly fragile. In light of these issues, it is important to comprehensively grasp regional land structure changes, improve the degree of land use, and reasonably allocate water resources to achieve the sustainable development of both the social economy and the ecological environment. This study uses the CA-Markov model, the PLUS model and the gray prediction model to simulate and validate land use/cover change (LUCC) in the Tarim River Basin, based on remote sensing data. The aim of this research is to discern the dynamic LUCC patterns and predict the evolution of future spatial and temporal patterns of land use. The study results show that grassland and barren land are currently the main land types in the Tarim River Basin. Furthermore, the significant expansion of cropland area and reduction in barren land area are the main characteristics of the changes during the study period (1992–2020), when about 1.60% of grassland and 1.36% of barren land converted to cropland. Over the next 10 years, we anticipate that land-use types in the basin will be dominated by changes in grassland and barren land, with an increasing trend in land area other than for cropland and barren land. Grassland will add 31,241.96 km2, mainly in the Dina River and the lower parts of the Weigan-Kuqu, Kashgar, Kriya, and Qarqan rivers, while barren land will decline 2.77%, with significant decreases in the middle and lower reaches of the Tarim River Basin. The findings of this study will provide a solid scientific basis for future land resource planning.

Multi-Dimensional Urbanization Coordinated Evolution Process and Ecological Risk Response in the Yangtze River Delta

The dislocated development of population, land, and economy will disturb the urban system, cause ecological risk problems, and ultimately affect regional habitat and quality development. Based on social statistics and nighttime lighting data from 2000 to 2018, we used mathematical statistics and spatial analysis methods to analyze the change process of urbanization’s coupling coordination degree and ecological risk response pattern in the Yangtze River Delta. Results show that: ① From 2000 to 2018, the coupling coordination degree of urbanization in the Yangtze River Delta increased, with high values in Suzhou-Wuxi-Changzhou, Shanghai, Nanjing and Hangzhou regions. ② The ecological risk in the Yangtze River Delta weakened, and the vulnerability and disturbance of landscape components together constitute the spatial differentiation pattern of regional ecological risk, which presented homogeneous aggregation and heterogeneous isolation. ③ The overall ecological stress of urbanization in the Yangtze River Delta decreased. ④ The population aggregation degree, socio-economic development level and built-up area expansion trend contributed to the spatiotemporal differentiation of urbanization’s ecological risks through the synergistic effects of factor concentration and diffusion, population quality cultivation and improvement, technological progress and dispersion, industrial structure adjustment and upgrading. This study can provide a reference for regional urbanization to deal with ecological risks reasonably and achieve high-quality development.

Spatial and Temporal Variation, Simulation and Prediction of Land Use in Ecological Conservation Area of Western Beijing

Exploring land use change is crucial to planning land space scientifically in a region. Taking the ecological conservation area (ECA) in western Beijing as the study area, we employ ArcGIS 10.2, landscape pattern index and multiple mathematical statistics to explore the temporal and spatial variation of land use from 2000 to 2020. Patch-generating Land Use Simulation (PLUS), Future Land Use Simulation (FLUS) and Markov models were used to simulate and predict the current land use in 2020. The models were evaluated for accuracy, and the more accurate PLUS model was selected and used to simulate and predict the potential land use in the study area in 2030 under two management scenarios. The main findings of this research are: (1) From 2000 to 2020, the construction land increased constantly, and the area of cultivated land and grassland decreased significantly. (2) For predicting the spatial distribution of land use in the study area, the PLUS model was more accurate than the FLUS model. (3) The land-use prediction of the study area in 2030 shows that the area of grassland, forest and water is approximately equal to their corresponding value in 2020, but the construction land increased constantly by occupying the surrounding cultivated land. According to this research, the continuous decrease of cultivated land in favor of increasing construction land will cause losses to the ecological service function of the ECA, which is not beneficial to the sustainable development of the region. Relevant departments should take corresponding measures to reduce this practice and promote sustainable development, particularly in the southern and western areas of the ECA where there is less construction land.

The Direct and Spillover Effect of Multi-Dimensional Urbanization on PM2.5 Concentrations: A Case Study from the Chengdu-Chongqing Urban Agglomeration in China

The Chengdu-Chongqing urban agglomeration (CUA) faces considerable air quality concerns, although the situation has improved in the past 15 years. The driving effects of population, land and economic urbanization on PM_2.5 concentrations in the CUA have largely been overlooked in previous studies. The contributions of natural and socio-economic factors to PM_2.5 concentrations have been ignored and the spillover effects of multi-dimensional urbanization on PM_2.5 concentrations have been underestimated. This study explores the spatial dependence and trend evolution of PM_2.5 concentrations in the CUA at the grid and county level, analyzing the direct and spillover effects of multi-dimensional urbanization on PM_2.5 concentrations. The results show that the mean PM_2.5 concentrations in CUA dropped to 48.05 μg/m³ at an average annual rate of 4.6% from 2000 to 2015; however, in 2015, there were still 91% of areas exposed to pollution risk (>35 μg/m³). The PM_2.5 concentrations in 92.98% of the area have slowly decreased but are rising in some areas, such as Shimian County, Xuyong County and Gulin County. The PM_2.5 concentrations in this region presented a spatial dependence pattern of "cold spots in the east and hot spots in the west". Urbanization was not the only factor contributing to PM_2.5 concentrations. Commercial trade, building development and atmospheric pressure were found to have significant contributions. The spillover effect of multi-dimensional urbanization was found to be generally stronger than the direct effects and the positive impact of land urbanization on PM_2.5 concentrations was stronger than population and economic urbanization. The findings provide support for urban agglomerations such as CUA that are still being cultivated to carry out cross-city joint control strategies of PM_2.5 concentrations, also proving that PM_2.5 pollution control should not only focus on urban socio-economic development strategies but should be an integration of work optimization in various areas such as population agglomeration, land expansion, economic construction, natural adaptation and socio-economic adjustment.

Spatio-Temporal Evolution and Mechanism Analysis of China’s Regional Innovation Efficiency

Improving regional innovation efficiency is the key to developing an innovative country. Exploring the spatio-temporal evolution characteristics of regional innovation efficiency is crucial in the formulation of regional policies and the choice of innovation models. This study used the superdata envelopment analysis method with undesirable outputs in evaluating the innovation efficiency of Chinese provinces. To assess the spatial spillover effects of innovation factors, the spatial autocorrelation and spatial Durbin model were adopted to characterize the spatio-temporal evolution, spatial correlation, and mechanisms of innovation efficiency. The highlights of the results are as follows: (1) The time-series changes in innovation efficiency showed a general trend from declining to increasing. (2) There were pronounced regional differences in innovation efficiency. The innovation efficiencies at the provincial level evolved from being decentralized to concentrated. The innovation efficiency was relatively stable in the eastern region and increased significantly in the central and western regions. The east–center–west evolution pattern gradually weakened. (3) The innovative efficiency exhibited spatial dependence, and the spatial agglomeration continued to increase. The extent of hot spots expanded, while cold spots shrunk slightly. (4) The scientific research environment, entrepreneurial environment, labor quality, and market environment were the essential elements that improved innovation efficiency. The impact of the different factors on innovation efficiency at different periods exhibited significant spatial heterogeneity.