Search

Fine particulate matter (PM2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM2.5 pollution in the study area, the PM2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM2.5, while elevation makes a strong negative impact on PM2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.

China is now the world's largest energy consumer, but severe air pollution problems have brought greater pressure to the production and development of its domestic economy. As an unavoidable result of air pollution, PM2.5 emissions are increasing. Previous literature has focused more on the impact of PM2.5 on the micro-level such as resident health and company location, yet macro-pattern studies between PM2.5 and innovation are inadequate. To bridge this gap, our research uses a spatial dynamic panel data model to systematically investigate the internal and external effects of PM2.5 concentration on innovation in China during the period 2001–2016. After forming a dataset of real-time PM2.5 concentration from satellite detection and using an innovation index instead of patents, we find a stronger spatial linkage between PM2.5 concentration and innovation. Thus, PM2.5 inhibits regional innovation significantly, and this result still exists after using the air mobility index as an instrument variable to alleviate endogenous problems. Lastly, PM2.5 concentration in neighboring regions also impedes local innovation considerably, indicating a spatial ripple effect of PM2.5. [ABSTRACT FROM AUTHOR]

Exploring the nonlinear effect of urbanization on PM 2.5 concentration and its driving mechanism is crucial for controlling urban air pollution. Based on remote sensing data and statistical data from 2002 to 2020, spatial autocorrelation, systematic dynamic panel regression, and spatio-temporal geographical weighted regression models were used to analyze the spatio-temporal evolution of PM 2.5 concentration in the urban agglomeration of the middle reaches of the Yangtze River and explore the driving mechanism of urbanization on PM 2.5 concentration at different spatial scales. The results showed that:① PM 2.5 concentration in the middle reaches of the Yangtze River showed an overall decreasing trend from 2002 to 2020, with a spatial distribution pattern of "high in the north and low in the south." ② Hot spot cities expanded towards the western part of the urban agglomeration, whereas cold spot cities showed enhanced spatial correlation. ③ The relationship between PM 2.5 concentration and economic, land, and population urbanization followed N-shaped, U-shaped, and U-shaped curves, respectively. Secondary industry and energy consumption significantly promoted the change in PM 2.5 concentration, and precipitation and vegetation helped mitigate PM 2.5 pollution. ④ The overall driving effects of all urbanization factors in local areas were transformed, and the main areas of influence were concentrated in the southeast, northwest, and southwest of the study area. Considering the current urban development status and regional characteristics of the urban agglomeration in the middle reaches of the Yangtze River, promoting green industrial transformation, rational planning of urban spatial distribution and population distribution, and enhancing infrastructure construction will facilitate the coordinated development between urbanization and environmental protection.

In the past few years, China's air quality, particularly PM2.5 concentrations, has received extensive attention. China is increasingly dependent on imported oil, and the international oil price fluctuations influence the air quality by two paths. A rise in oil prices puts pressure on the economy and reduces energy consumption, which could improve air quality. However, the substitution effect by high oil prices tends to increase the use of environmentally unfriendly energies, which worsens air quality. In this study, the authors employ wavelet analysis to determine how international oil price fluctuations affect PM2.5 concentrations in China. The authors process a sample of 12 typical Chinese cities, which are discretely distributed in the northeast, north, east, central, south, and southwest of China. The results show that in most cities international crude oil prices are positively correlated to the PM2.5 concentrations in the short term (1–4 months) and that the fluctuations in oil prices are usually ahead of the changes of PM2.5 concentrations. It is more pronounced in industrially developed cities such as in Shanghai. An extension of the study to include the country data yields more consistent findings. Empirical analysis indicates that, in the short run, the substitution effect caused by oil price fluctuations exerts a stronger impact on PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

The identification of fine particulate matter (PM2.5) concentrations and its driving factors are crucial for air pollution prevention and control. The factors that influence PM2.5 in different regions exhibit significant spatial heterogeneity. Current research has quantified the spatial heterogeneity of single factors but fails to discuss the interactions between factors. In this study, we first divided the study area into subregions based on the spatial heterogeneity of factors in a multi-scale geographically weighted regression model. We then investigated the interactions between different factors in the subregions using the geographical detector model. The results indicate that there was significant spatial heterogeneity in the interactions between the driving factors of PM2.5. The interactions between natural factors have significant uncertainty, as do those between the normalized difference vegetation index (NDVI) and socioeconomic factors. The interactions between socioeconomic factors in the subregions were consistent with those in the whole region. Our findings are expected to deepen the understanding of the mechanisms at play among the aforementioned drivers and aid policymakers in adopting unique governance strategies across different regions.

With the acceleration of urbanization and industrialization, atmospheric pollution has become a major issue, restricting the sustainable development of the urban environment. Since 2013, Beijing has been among China's most seriously affected regions in terms of haze pollution. Atmospheric pollution is closely linked to land use, particularly the spatial patterns of green and urban land. Therefore, the quantification of the relationship between fine particulate matter (PM2.5) concentration and its driving factors in Beijing is of considerable significance for environmental management and spatial epidemiological studies. A land use regression (LUR) model was constructed to simulate the spatio-temporal distribution of PM2.5 concentration. In this study, the independent variables (driving factors) included land use, meteorological factors, population, roads, the digital elevation model, and the normalized difference vegetation index. The five models had adjusted R2 of 0.887, 0.770, 0.742, 0.877, and 0.798, respectively. Land use and meteorological factors were the main factors affecting PM2.5 concentration. The driving factors of land use on a large scale and roads on a small scale had a significant impact on PM2.5 emissions. Beijing's PM2.5 concentrations in 2015 showed clear spatio-temporal characteristics. The highest (lowest) average PM2.5 concentration was recorded in winter (summer). In terms of spatial distribution, PM2.5 concentrations showed a "low in the northwest and high in the southeast" trend. The most polluted areas were mainly distributed in the central city and the southeastern and southwestern regions. The PM2.5 concentration boundary was essentially consistent with the boundary of land use type. Different land use types promoted or inhibited PM2.5 concentrations, with a difference of more than 20 μg/m3 PM2.5 between the two land use categories. Thus, PM2.5 concentrations should be controlled by optimizing the spatial and temporal patterns of land use. [ABSTRACT FROM AUTHOR]

Continuous improvement in remote sensing observation-based techniques has enabled data inversion assimilation methods to play an increasingly important role in the field of numerical forecasting. Using an artificial intelligence fusion inversion method can overcome the multisource heterogeneity of data, thereby providing a better blended source for data assimilation (DA) and improving the forecasting capability. This study proposed a multimodel stacking machine learning algorithm to estimate surface concentrations of PM 2.5 (particulate matter with an aerodynamic equivalent diameter of ≤2.5 μm) using Himawari-8 satellite data. The derived satellite-based PM 2.5 estimation was then blended with the in situ observations as physical constraints to obtain a high-resolution multisource blended dataset. To determine the level of improvement provided by the blended dataset, four parallel experiments were conducted during February 2022: a control experiment without DA (noDA), an experiment that assimilated satellite-based estimation dataset (sate-basedDA), an experiment that assimilated multisource blended dataset (blendDA) and an experiment that assimilated in situ observations (siteDA) based on the Gridpoint Statical Interpolation system. Statistically, in comparison with the direct satellite-based PM 2.5 estimations, the blended dataset better matched the in situ observations. The accuracy of PM 2.5 predictions can be optimized by DA and for the first 24-h period the blended dataset performed better than the traditional ground observations as the assimilation source. Throughout the initial 24-h period, the results of blendDA (siteDA, sate-basedDA) showed an improvement in terms of root-mean-square error, with reductions varying from 6.65 to 20.20 μg/m3 (4.50–19.79 μg/m3, 2.29–10.86 μg/m3). • A new multimodel stacking algorithm for PM 2.5 estimations was established. • The estimations were fused with in situ observations to construct blended dataset by a proposed physical constrain method. • Different datasets were used as assimilation sources in three parallel experiments to evaluate the simulation effects. • The accuracy of PM2.5 predictions can be greatly optimized by using the blended dataset as the assimilation source. [ABSTRACT FROM AUTHOR]

The increase in atmospheric pollution dominated by particles with an aerodynamic diameter smaller than 2.5 μm (PM2.5) has become one of the most serious environmental hazards worldwide. The geographically weighted regression (GWR) model is a vital method to estimate the spatial distribution of the ground-level PM2.5 concentration. Wind information reflects the directional dependence of the spatial distribution, which can be abstracted as a combination of spatial and directional non-stationarity components. In this paper, a GWR model considering directional non-stationarity (GDWR) is proposed. To assess the efficacy of our method, monthly PM2.5 concentration estimation was carried out as a case study from March 2015 to February 2016 in the Yangtze River Delta region. The results indicate that the GDWR model attained the best fitting effect (0.79) and the smallest error fluctuation, the ordinary least squares (OLS) (0.589) fitting effect was the worst, and the GWR (0.72) and directionally weighted regression (DWR) (0.74) fitting effects were moderate. A non-stationarity hypothesis test was performed to confirm directional non-stationarity. The distribution of the PM2.5 concentration in the Yangtze River Delta is also discussed here.

Real estate investment has been an important driving force in China’s economic growth in recent years, and the relationship between real estate investment and PM2.5 concentrations has been attracting widespread attention. Based on spatial econometric modelling, this paper explores the relationships between real estate investment and PM2.5 concentrations using multi-source panel data from 30 provinces in China between 1987 and 2017. The results demonstrate that compared with static spatial panel modelling, using a dynamic spatial Durbin lag model (DSDLM) more accurately reflects the influences of real estate investment on PM2.5 concentrations in China, and that PM2.5 concentrations show significant superposition effects and spillover effects. Moreover, there is an inverted U-shaped relationship between real estate investment and PM2.5 concentrations in the Eastern and Central Regions of China. At the national level, the impacts of real estate investment on land urbanization and PM2.5 concentrations first increased and then decreased over time. The key implications of this analysis are as follows. (1) it highlights the need for a unified PM2.5 monitoring platform among Chinese regions; (2) the quality of population urbanization rather than land urbanization should be given more attention; and (3) the speed of construction of green cities and building of green transportation systems and green town systems should be increased.

In contrast to the surge of attention to China's business registration reform per se , the environmental externalities of the reform have been somewhat neglected by mainstream academics. Adopting the difference-in-differences method alongside propensity-score matching, and utilizing the panel data of 283 cities in China from 2000 to 2013, this paper investigates the impacts of the deregulation reform on PM2.5 concentrations. The results indicate that the business registration reform has contributed to PM2.5 air pollution, with above-average impacts in central areas, but insignificant effects in western China. Furthermore, the treatment effects are stronger in cities with larger entry of small and medium enterprises and greater pressure to achieve targets for local GDP growth. Aiming to draw public attention to the problems arising from the reform, our study advocates necessary environmental regulations on business registration, which lends support to the theoretical view of "public interest" rather than "public choice". • Deregulation reform of business registration increases PM2.5 concentrations in China. • Treatment effects of deregulation reform on PM2.5 air pollution vary widely among regions. • More SME entries and higher targets for GDP growth are associated with stronger effects of deregulation reform on PM2.5. • PM2.5 air pollution should be considered during the deregulation reform in China and other developing economies. [ABSTRACT FROM AUTHOR]

Abstract China's rapid urbanization has aggravated its air pollution problems and subsequently results in increased interests in the relationship between urbanization and PM 2.5 concentrations. Currently, research on the ways that urbanization directly influences air quality are comparatively mature; however, quantification of the spillover effect of urbanization on PM 2.5 concentrations in adjacent regions remains understudied. Additionally, urbanization is a multi-dimensional phenomena in China and it is important to distinguish the ways in which the multiple aspects of urbanization influence PM 2.5 concentrations. Taking the Beijing-Tianjin-Hebei Urban agglomeration of China as an example, this study explored the spatio-temporal change and spatial dependence pattern of PM 2.5 concentrations, and further examined and compared the unique impacts of the multiple dimensions of urbanization on PM 2.5 concentrations. The results showed that spatial dependence of PM 2.5 generally took place over the range of 200 km in the study area. Economic urbanization had a much stronger influence on air quality than land urbanization or population urbanization; it was determined to be the best indicator to represent the impact of urbanization, especially the spillover effect of urbanization on PM 2.5 concentrations in adjacent regions. Over the period 2000–2010, the impacts from both local and neighboring economic urbanization on PM 2.5 concentrations increased significantly. In contrast, land urbanization mainly exerted influence on air quality in focal regions. Population urbanization showed no significant direct or spillover effects on PM 2.5 concentrations in the whole urban agglomeration. This study provides evidence that the influence of urbanization within and across cities must be considered when conducting joint air pollution control in an urban agglomeration. PM 2.5 taxes based on the diffusion and transfer of environment pressures are one possible policy mechanism that could help to account for the spillover effect of economic urbanization on air quality. System-based approaches and flow-centered governance are two suggested ways to coordinate the management of both urbanization and air quality. Highlights • Spatial dependence of PM 2.5 concentrations occurs over the range of 200 km in BTH. • Spillover effect of urbanization on PM 2.5 occurs through various approaches. • Development of economic, land, population urbanization is inconsistent in BTH. • Economic urbanization can best indicate the impact of urbanization on PM 2.5. [ABSTRACT FROM AUTHOR]

Abstract To protect public health by providing an early warning, PM 2.5 concentration forecasting is an essential and effective work. In this paper, an ensemble long short-term memory neural network (E-LSTM) is proposed for hourly PM 2.5 concentration forecasting. The presented model is implemented using three steps: (1) ensemble empirical mode decomposition (EEMD) is firstly utilized for multi-modal feature extraction, (2) long short-term memory approach (LSTM) is then employed for multi-modal feature learning, and (3) inverse EEMD computation is finally used for multi-modal feature estimated integration. In each modeling process, the mode information of the PM 2.5 and the corresponding meteorological variables in 1-h advance are utilized as inputs to forecast the next mode information of the PM 2.5 concentration. To evaluate the performance of the E-LSTM model, two datasets collected from two environmental monitoring stations in Beijing, China, are investigated. It is demonstrated that the E-LSTM model inspired by ensemble learning, which constructs multiple LSTMs in different modes, obtained better forecasting performance than that using the single LSTM and feed forward neural network in terms of mean absolute percentage error (19.604% and 16.929%), root mean square error (12.077 μg m−3 and 13.983 μg m−3), and correlation coefficient criteria (0.994 and 0.991) respectively. Highlights • An E-LSTM approach is proposed to forecast hourly PM 2.5 concentration. • EEMD is applied for mode transformation and feature extraction. • Multiple LSTMs are modeled for ensemble learning for multi-mode feature. • Multi-mode features estimated are integrated using inverse EEMD computation. • This model can get better forecasting performance. [ABSTRACT FROM AUTHOR]

Abstract Few attempts have been made to systematically investigate the impacts of urbanization on PM 2.5 concentrations in countries at different stages of economic development. In this study, a broad concept of urbanization that considers the transformations in the urban economy and the transport sector induced by urbanization is proposed to investigate the influence of urbanization on national PM 2.5 concentrations for underdeveloped, developing and developed countries during 1998–2014. The results indicate that urbanization has a significant relationship with PM 2.5 concentrations, but the magnitude of its influence varies among groups of countries with different development levels. First, the positive response of PM 2.5 concentrations to increased urbanization and transport-related emissions in underdeveloped countries are noticeably stronger than that in developing and developed countries. Second, for developing countries, urbanization, transport-related emissions and industrialization all have a significant positive effect on national PM 2.5 concentrations increase, although their impacts are unexpectedly smaller than those in the other groups of countries. Finally, increasing urbanization and the decrease in CO 2 emissions from manufacturing industry appear to reduce national average PM 2.5 concentrations in developed countries, while the decrease in transport-related CO 2 emission is likely to cause the rise in national average PM 2.5 concentrations. Graphical abstract To comprehensively understand the impacts of urbanization on PM 2.5 concentrations in countries at different stages of economic development, a new research framework that focuses on three dimensions in a broader concept of urbanization, including population migration, economic transformation, and motorized transportation, was established. Image 1 Highlights • Urbanization has a significant relationship with PM 2.5 concentrations, but its magnitude varies among regions. • The response of PM 2.5 concentrations to increased urbanization in underdeveloped group are noticeably higher. • For developing countries, impacts of all factors are smaller than in developed countries. • Increased urbanization and transport emission in developed countries have a negative impact on PM 2.5 concentrations. [ABSTRACT FROM AUTHOR]

Abstract Fine particulate matter (PM 2.5) pollution in Eastern China (EC) has raised concerns due to its adverse effects on air quality, climate, and human health. This study investigated the long-term variation trend in satellite-derived PM 2.5 concentrations and how it was related to pollutant emissions and meteorological parameters over EC and seven regions of interest (ROIs) during 1998–2016. Over EC, the annual mean PM 2.5 increased before 2006 due to the enhanced emissions of primary PM 2.5 , NO x and SO 2 , but decreased with the reduced SO 2 emissions after 2006 evidently in response to China's clean air policies. In addition, results from statistical analyses indicated that in the North China Plain (NCP), Northeast China (NEC), Sichuan Basin (SCB) and Central China (CC) planetary boundary layer height (PBLH) was the dominant meteorological driver for the PM 2.5 decadal changes, and in the Pearl River Delta (PRD) wind speed is the leading factor. Overall, the variation in meteorological parameters accounted for 48% of the variances in PM 2.5 concentrations over EC. The population-weighted PM 2.5 over EC increased from 36.4 μg/m3 in 1998–2004 (P1) to 49.4 μg/m3 in 2005–2010 (P2) then decreased to 46.5 μg/m3 in 2011–2016 (P3). In the NCP and NEC, the percentages of the population living above the World Health Organization (WHO) Interim Target-1 (IT-1, 35 μg/m3) have risen steadily over the past 20 yr, reaching maxima of 97.3% and 78.8% in P3, respectively, but decreases of ∼30% from P2 to P3 were found for the SCB and PRD. Graphical abstract Image 1 Highlights • The joint effect of emission and meteorological determined the decadal trend of PM 2.5 • The changes in meteorological parameters explained 48% of the variances in PM 2.5. • Planetary boundary layer height was the dominant driver for the decadal changes of PM 2.5 • Eastern China has experienced slight decreases in the population-weighted mean PM 2.5 over the past 10 year. The joint effect of emissions and meteorological determined the decadal trend in PM 2.5. The decadal variation in meteorological parameters accounted for 48% of the variances in PM 2.5 concentrations over Eastern China. Eastern China has experienced slight decreases in the population-weighted mean PM 2.5 over the past 10 year. [ABSTRACT FROM AUTHOR]

Abstract China experiences severe particulate matter pollution associated with rapid economic growth and accelerated urbanization. In this study, concentrations of PM 2.5 (fine particulate matter with an aerodynamic diameter ≤ 2.5 μm) throughout China, and specifically in nine typical urban agglomerations and one economic region, were statistically analyzed using high-resolution ground-based PM 2.5 observations from June 2014 to May 2018. The spatial variation of PM 2.5 was also explored via spatial autocorrelation analysis. High annual mean PM 2.5 concentrations were predominantly concentrated in the Beijing-Tianjin-Hebei, Central Plain, Northern Slope of Tianshan Mountain, and Cheng-Yu urban agglomerations, as well as the Huaihai Economic Region. The proportion of air quality nationwide monitoring sites where annual average PM 2.5 concentrations exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II annual standard were 82.8%, 77.1%, and 70.8% in 2015, 2016, and 2017, respectively. Moreover, the frequency of PM 2.5 concentrations meeting the CAAQS Grade I 24-h standard increased in five national-level urban agglomerations, and the average annual PM 2.5 decreased from 2015 to 2017 with a reduction rate of over 20%. The southern Beijing-Tianjin-Hebei agglomeration and surrounding areas revealed the highest PM 2.5 pollution in four seasons. Monthly mean PM 2.5 typically exhibited a characteristic "U" shape. Diurnal mean PM 2.5 concentrations were generally consistent with typical urban agglomerations, with maximum and minimum PM 2.5 values occurring at approximately 08:00–12:00 and 15:00–17:00, respectively, except for the Northern Slope of Tianshan Mountain urban agglomeration (NSTM-UA) (14:00 and 08:00, respectively). A positive spatial autocorrelation of PM 2.5 concentrations was observed in all urban agglomerations (except NSTM-UA); high-high agglomeration centers of PM 2.5 pollution were located far inland with a circular distribution, and low-low agglomeration centers formed at the periphery of the high-high agglomeration region. This study is key for understanding the difference in PM 2.5 concentrations among urban agglomerations and region-oriented air pollution control strategies are highly suggested. Graphical abstract Unlabelled Image Highlights • Spatiotemporal patterns of PM 2.5 over typical urban agglomerations in China were explored. • The annual mean PM 2.5 decreased in national-level urban agglomerations from 2015 to 2017. • PM 2.5 monthly mean values were higher at the beginning and end of the year than in the middle. • The key to PM 2.5 control is regional joint effort for the strong spatial autocorrelation. • PM 2.5 control still faces challenge for over 70% sites exceeding national annual limit (35 μg/m3). [ABSTRACT FROM AUTHOR]

Abstract The aim of this paper is to estimate the effects of natural conditions and anthropogenic factors on PM 2.5 concentrations, taking into consideration differences in the income levels, and thus the development stages, of the cities studied. To achieve this goal, a balanced dataset of 287 Chinese cities was divided into different income-based panels for the period 1998–2015. The empirical estimation results indicated that meteorological conditions exerted varied effects on PM 2.5 concentrations across different income-based panels. The results show that the coefficients of temperature were positive and significant in all panels, with the exception of upper-middle-income cities. Whilst wind speed and precipitation were found to be conducive to reducing PM 2.5 concentrations, no such significant correlation was found in relation to relative humidity (except in high-income cities). In terms of the anthropogenic factors addressed in the study, we found an inverted U-shaped relationship between economic development and PM 2.5 concentrations, confirming the Environmental Kuznets Curve hypothesis. In addition, the industrial structure and road density were observed to exert significant positive impacts on PM 2.5 concentrations. The empirical analysis of the effects of FDI on PM 2.5 concentrations indicate that FDI aggravated PM 2.5 pollutions in the total cities and lower-middle-income cities panels, supporting the Pollution Haven Hypothesis. The empirical results for population density suggested that it does not significantly influence PM 2.5 concentrations. Moreover, we found that built-up area exerts mixed effects on PM 2.5 concentrations. These results cast a new light on the issue of PM 2.5 pollution for government policy makers tasked with formulating measures to mitigate the concentration of such pollutants, encouraging that consideration be given to the differences between cities with different income levels. Graphical abstract Unlabelled Image Highlights • The paper estimated the influence of natural conditions and anthropogenic factors on PM2.5 concentrations. • Cities with different income levels were considered in this paper. • Both meteorological conditions and anthropogenic exerted varied effects on PM2.5 concentrations across different income-based panels. • Targeted measures should be employed to mitigate PM2.5 concentrations in different income level cities. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this study is to explore the impact of socioeconomic factors on PM 2.5 concentrations and to provide insights into air quality improvement. We firstly studied the spatial autocorrelations of PM 2.5 concentrations using global Moran's I and Local Indicators of Spatial Association, then explained the spatial heterogeneity of regional PM 2.5 concentrations and identify the driving factors on PM 2.5 by geographical detector technique, using data extracted from satellite observations over the years from 2000 to 2015. The results showed that, the annual average PM 2.5 concentration in China ranged from 11.5 μg/m³ to 18.7 μg/m³ with an upward trend in general, while PM 2.5 pollution were relatively serious in Beijing-Tianjin-Hebei region and Yangtze River Delta. Regional PM 2.5 concentrations showed significant global and local spatial autocorrelation. Regions of high PM 2.5 concentrations tend to cluster with regions of similar PM 2.5 concentrations. From a long-term perspective, population density has the greatest power of determinant on PM 2.5 , followed by electricity consumption, industry structure, coal consumption, number of vehicles per capita and GDP per capita. Over the study period, the impact of population density revealed a trend to first rise and then fall, and the impacts of GDP per capita showed a slightly upward trend. The impact trends of number of vehicle per capita and industry structure presented to be fluctuated. The determinant power of coal consumption and electricity consumption had significant downward trends. Highlights • Global and local spatial autocorrelations of PM 2.5 pollution exist in China. • Population density has the highest power of determinant on PM 2.5 concentration. • Coal consumption and electricity consumption have downward impact on PM 2.5 pollution. [ABSTRACT FROM AUTHOR]

Abstract Air quality deteriorates fast under urbanization in recent decades. Reliable and precise regional multi-step-ahead PM 2.5 forecasts are crucial and beneficial for mitigating health risks. This work explores a novel framework (MM-SVM) that combines the Multi-output Support Vector Machine (M-SVM) and the Multi-Task Learning (MTL) algorithm for effectively increasing the accuracy of regional multi-step-ahead forecasts through tackling error accumulation and propagation that is commonly encountered in regional forecasting. The Single-output SVM (S-SVM) is implemented as a benchmark. Taipei City of Taiwan is our study area, where three types of air quality monitoring stations are selected to represent areas imposed with high traffic influences, high human activities and commercial trading influences, and less human interventions close to nature situation, respectively. We consider forecasts of PM 2.5 concentrations as a function of meteorological and air quality factors based on long-term (2010–2016) observational datasets. Firstly, the Kendall tau coefficient is conducted to extract key spatiotemporal factors from regional meteorological and air quality inputs. Secondly, the M-SVM model is trained by the MTL to capture non-linear relationships and share correlation information across related tasks. Lastly, the MM-SVM model is validated using hourly time series of PM 2.5 concentrations as well as meteorological and air quality datasets. Regarding the applicability of regional multi-step-ahead forecasts, the results demonstrate that the MM-SVM model is much more promising than the S-SVM model because only one forecast model (MM-SVM) is required, instead of constructing a site-specific S-SVM model for each station. Moreover, the forecasts of the MM-SVM are found better consistent with observations than those of any single S-SVM in both training and testing stages. Consequently, the results clearly demonstrate that the MM-SVM model could be recommended as a novel integrative technique for improving the spatiotemporal stability and accuracy of regional multi-step-ahead PM 2.5 forecasts. Graphical abstract Unlabelled Image Highlights • A novel MM-SVM hybriding M-SVM and multi-task learning for regional PM 2.5 forecasts. • Multi-task learning algorithm effectively optimizes parameters of the M-SVM model. • MM-SVM model overcomes the instabilities of spatiotemporal forecasting. • MM-SVM identifies heterogeneities in air pollutant-generating mechanisms and seasons. • MM-SVM increases multi-step-ahead PM 2.5 forecasts accuracy and applicability. [ABSTRACT FROM AUTHOR]

Atmospheric particulate matter (PM2.5) seriously influences air quality. It is considered one of the main environmental triggers for lung and heart diseases. Air pollutants can be adsorbed by forest. In this study we investigated the effect of forest cover on urban PM2.5 concentrations in 12 cities in Heilongjiang Province, China. The forest cover in each city was constant throughout the study period. The average daily concentration of PM2.5 in 12 cities was below 75 μg/m3 during the non-heating period but exceeded this level during heating period. Furthermore, there were more moderate pollution days in six cities. This indicated that forests had the ability to reduce the concentration of PM2.5 but the main cause of air pollution was excessive human interference and artificial heating in winter. We classified the 12 cities according to the average PM2.5 concentrations. The relationship between PM2.5 concentrations and forest cover was obtained by integrating forest cover, land area, heated areas and number of vehicles in cities. Finally, considering the complexity of PM2.5 formation and based on the theory of random forestry, we selected six cities and analyzed their meteorological and air pollutant data. The main factors affecting PM2.5 concentrations were PM10, NO2, CO and SO2 in air pollutants while meteorological factors were secondary. [ABSTRACT FROM AUTHOR]

Abstract Atmospheric haze from particulate matter has become a major health concern in many large and medium-sized cities of China. Fine airborne particles, PM 2.5 , are a major component of haze, and the reasons for their fluctuating concentrations need to be better understood to improve air quality. While much of the air quality data from the China National Environmental Monitoring Centre (CNEMC) has already been analyzed for larger Chinese cities such as Beijing and Shanghai, the data from Fuzhou is the topic of this study. Time series data of pollutant concentrations and meteorological parameters were accessed for Fuzhou from August 2014 to July 2016, covering two cold periods (November through February) and two warm periods (May through July). PM 2.5 concentrations were compared with those of other pollutants and with meteorological parameters, and the AutoRegressive Integrated Moving Average (ARIMA) model was applied to forecast PM 2.5 concentrations. The correlation analysis of Spearman's rho showed that PM 2.5 concentrations had significant positive correlations with PM 10 , SO 2 and NO 2 concentrations, while they had negative correlations with meteorological parameters. The ARIMA results showed that PM 2.5 concentrations experienced seasonal fluctuations over the two years, which were higher in the cold periods and lower in the corresponding warm periods, ranging from 23 to 52 μg/m3 and from 19 to 31 μg/m3, respectively. Average PM 2.5 concentrations during these same cold periods and warm periods were 35 μg/m3 over the two cold periods, which was 52% higher than the 23 μg/m3 averaged over the two warm periods. Compared to the two observed years, 2014–15 and 2015–16, the forecast of PM 2.5 concentrations for 2016–17, ranging from about 15 to 30 μg/m3, exhibited similar seasonal fluctuations but also reduced in the same period, ranging from 19 to 52 μg/m3. It is speculated that the Fuzhou Government's macro-policy on air quality may be one of the reasons for the decrease in PM 2.5 concentration during the cold periods. Possible causes were given for the discrepancies between actual and fit values. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The contribution of socioeconomic variables to PM 2.5 concentrations varies across countries with different income levels. • For lower-middle-income and low-income countries, the most important factor is the energy consumption structure. • Economic growth better explains PM 2.5 concentrations in high-income and upper-middle countries. • The impact of urbanization on PM 2.5 concentrations was limited in each panel. • PM 2.5 concentrations have a significantly path-dependent character. Abstract Despite the fact that the relationship between socioeconomic development and PM 2.5 concentrations has drawn much attention from multidisciplinary scholars in recent years, the causal links between PM 2.5 concentrations and energy consumption, energy intensity, economic growth, and urbanization in countries with different income levels remain poorly understood. The present study categorized countries into four panels based on their income levels, in order to investigate the casual relationship between energy consumption, energy intensity, economic growth, urbanization, and PM 2.5 concentrations for the period 1998–2014. To achieve this goal, balanced panel data and econometric methods were utilized. The results revealed that cointegration relationships existed between PM 2.5 concentrations and the variables studied, in all panels. Findings of a panel Granger causality test based on a Vector Error-Correction Model showed that energy consumption, energy intensity, economic growth, and urbanization led to increased PM 2.5 concentrations in the long term. Economic growth was the principal variable that impacted on PM 2.5 concentrations in the global panel, the high-income panel, and the upper-middle income panel. PM 2.5 concentrations can, we argue, be decreased by improving energy intensity in the short term in all countries except those belonging to the low-income group. In contrast, reducing the urbanization level in the short term is not an efficient way to mitigate PM 2.5 concentrations. Our findings further indicated that the energy consumption structure was the greatest factor impacting on PM 2.5 concentrations in lower-middle-income and low-income countries. [ABSTRACT FROM AUTHOR]

Abstract Fluctuations of ambient fine particulate matter (PM 2.5) concentrations show clear yearly and weekly patterns, which has been revealed by previous studies. However, reliability of those studies may be affected by their small research areas, short observation periods, and/or the lack of using specialized statistical approaches for time series. The current study applies a recently developed time-series analysis procedure, Prophet, to investigate seasonality of daily PM 2.5 concentrations over nine years (2007–2015) measured at 220 monitoring stations across the United States. Prophet is a new tool for producing high quality forecasts from time series data that have characteristics of multiple temporal patterns with either linear or non-linear growth/decline. Through decomposing each PM 2.5 time series into three major components (i.e., trend, seasonality, and holidays), we observed periodically changing patterns of PM 2.5 concentrations weekly and yearly consistent with previous findings. Specifically, relatively high PM 2.5 concentrations tend to appear in the month of January and on Fridays, and PM 2.5 concentrations on Sunday are generally lower than those on most other days of the week. However, we discovered that high PM 2.5 concentrations are also likely to appear in July. Additionally, compared to Fridays in most studies, the highest PM 2.5 concentrations are found to more likely occur on Saturdays, while the lowest concentrations are found on Monday as universally as on Sunday. Beyond understanding the seasonality of PM 2.5 concentrations, this study revealed the potential use of Prophet, originally designed for business time series, for detecting periodicities of environmental phenomena. Highlights • Seasonality of PM 2.5 concentrations over the United States was detected. • A new time-series analysis procedure, Prophet, was used to reveal the seasonality. • Yearly highest PM 2.5 is likely to appear in January and July. • Weekly highest PM 2.5 is most likely to appear on Saturday rather than Friday. • Besides Sunday, weekly lowest PM 2.5 is also likely to occur on Monday. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of fog and haze in China has attracted increasing attention in the field of electrical insulation. Fog–haze weather is an extreme weather condition with high humidity, high concentrations of suspended particulate matter, and sometimes high conductivity. This exacerbates insulator surface contamination and moistens the contamination layer, which may lead to flashover incidents. Here, an experimental system was established to simulate the fog–haze environment under controlled and tunable conditions. Simulated contamination tests were then conducted using three typical insulators. The influence of insulator type and environmental parameters was investigated. Test results showed that higher humidity led to faster pollution accumulation for all three insulators. Moreover, equivalent salt deposit density (ESDD) on the surface increased linearly with increasing fog conductivity, and that on the upper surface rose faster. The ESDD increased significantly under AC voltage, especially on the lower surface. Finally, insulator contamination was studied in a real‐world fog–haze environment with periodic measurement of the ESDD and environmental parameters. The ESDD showed a good correlation with the PM2.5 and PM10 concentrations. This work should have relevance for the external insulation of the power grid to resist the effects of fog and haze. [ABSTRACT FROM AUTHOR]

Exposure to PM2.5 results in negative effects on human health. However, PM2.5 exposure at the national scale is poorly known for China owing to limited spatial and temporal PM2.5 concentration data. In this study, we present analyses of PM2.5 exposure throughout China using high-resolution temporal and spatial ground-level PM2.5 data from 2015. Our results indicated that the annual mean PM2.5 concentration was 52.81 μg/m 3 , and that the highest annual mean PM2.5 concentrations primarily appeared in the North China Plain. We also found the lowest and highest monthly mean PM2.5 concentrations appeared in August and January, respectively, while the lowest and highest diurnal mean PM2.5 concentrations occurred at 16:00 and 10:00, respectively. Moreover, comparisons to data from 2013 indicated that the annual mean PM2.5 concentrations decreased by 12.31% from 2013 to 2015, which was likely due to the implementation of environmental protection laws in early 2015. Our findings provide new insights, for not only studies of PM2.5 exposure and human health, but also to inform the implementation of national and regional air pollution reduction policies. [ABSTRACT FROM AUTHOR]

Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., 2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015−2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM2.5 concentrations were 0.234−0.329 and 0.340−0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1−5 km, AOD at finer spatial scales generally had higher correlations with PM2.5 concentrations. However, at spatial scales 2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM2.5 measurements in regional air pollution studies.

Although a many studies concerning crop residue burning have been conducted, the influence of crop residue burning on local PM2.5 concentrations remains unclear. The number of crop residue burning spots was the highest in Heilongjiang province and we extracted crop residue burning spots for this region using MOD14A1 (Thermal Anomalies & Fire Daily L3 Global 1 km) data and national land cover data. By analyzing the temporal variation of crop residue burning and PM2.5 concentrations in Heilongjiang province, we found that the total number of crop residue burning spots was not correlated with the variations of PM2.5 concentrations at a provincial (regional) scale. However, crop residue burning exerted notable influence on the variations of PM2.5 concentrations at a local scale. We experimented with a set of buffer zone radiuses to examine the influencing area of crop residue burning. The results suggest that the valid influencing area of crop residue burning was between 50 and 80 km. The mean PM2.5 concentration measured at stations close to crop residue burning spots was more than 60 μg/m3 higher than that measured at stations not close to crop residue burning spots. However, no consistent, significant correlation existed between the existence of crop residue burning spots and local PM2.5 concentrations, indicating that local PM2.5 concentrations were influenced by a diversity of factors and not solely controlled by crop residue burning. This research also provides suggestions for better understanding the role of crop residue burning in local and regional air pollution. [ABSTRACT FROM AUTHOR]

As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PMPM2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA-GAM) was proposed to estimate PM2.5 concentrations in this study. The reliability of PCA-GAM for estimating PM2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA-GAM outperforms traditional LUR modelling with relatively higher adjusted R2 (0.94) and lower RMSE (4.08 μg/m3). The CV-adjusted R2 (0.92) is high and close to the model-adjusted R2, proving the robustness of the PCA-GAM model. The PCA-GAM model enhances PM2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA-GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution. [ABSTRACT FROM AUTHOR]

This article examines the effect of regional trade agreements (RTAs) with and without environmental provisions on the concentration of PM2.5 (fine particulate matter in diameter), which is one of the most significant air pollutants. Using panel data for 136 countries from 2001 to 2010, we estimate the effect of trade liberalization under RTAs and the effect of environmental regulations under RTAs with environmental provisions. Moreover, paired countries are used to examine the effect of RTAs with environmental provisions on the convergence of PM2.5 concentrations. Human behaviours and natural characteristics are added as control variables, and the potential endogeneity and environmental provisions of RTAs as well as the gross domestic product and openness of specific regions are addressed using instrumental variables or difference-in-differences after propensity score matching. The main results indicate that RTAs without environmental provision terms lead to worse air quality in terms of PM2.5 concentrations, whereas RTAs with environmental provision terms are likely to be associated with a lower level of PM2.5 concentrations. In addition, RTAs with environmental provisions, especially environmental cooperation provisions, facilitate the convergence of PM2.5 concentrations between contracting countries, which can be explained by the late-mover advantage after distinguishing North–South, South–South and North–North trade forms. [ABSTRACT FROM AUTHOR]

Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD). Satellite derived Aerosol Optical Depth (AOD) has been used to retrieve the spatial coverage of PM2.5 concentrations. To improve the retrieval accuracy of the daily AOD-PM2.5 model, various auxiliary variables like meteorological or geographical factors have been adopted into the Geographically Weighted Regression (GWR) model. However, these variables are always arbitrarily selected without deep consideration of their potentially varying temporal or spatial contributions in the model performance. In this manuscript, we put forward an automatic procedure to select proper auxiliary variables from meteorological and geographical factors and obtain their optimal combinations to construct four seasonal GWR models. We employ two different schemes to comprehensively test the performance of our proposed GWR models: (1) comparison with other regular GWR models by varying the number of auxiliary variables; and (2) comparison with observed ground-level PM2.5 concentrations. The result shows that our GWR models of "AOD + 3" with three common meteorological variables generally perform better than all the other GWR models involved. Our models also show powerful prediction capabilities in PM2.5 concentrations with only slight overfitting. The determination coefficients R² of our seasonal models are 0.8259 in spring, 0.7818 in summer, 0.8407 in autumn, and 0.7689 in winter. Also, the seasonal models in summer and autumn behave better than those in spring and winter. The comparison between seasonal and yearly models further validates the specific seasonal pattern of auxiliary variables of the GWR model in the YRD. We also stress the importance of key variables and propose a selection process in the AOD-PM2.5 model. Our work validates the significance of proper auxiliary variables in modelling the AOD-PM2.5 relationships and provides a good alternative in retrieving daily PM2.5 concentrations from remote sensing images in the YRD. [ABSTRACT FROM AUTHOR]

Urban form is related to PM 2.5 and has an impact on it. However, the relationships between urban form and PM 2.5 concentrations and their rules still need to be explored from the spatial pattern and process perspective. Using 84 cities in China during 2000–2020 as an example, our study analyzed the relationships and their laws from a spatial pattern and process perspective. A typical Kuznets inverted U function was proposed to quantitatively depict the overall relationship between urban compactness and PM 2.5 concentrations; the PM 2.5 concentrations first increased and then decreased with the increase in urban compactness. A Density Concentration Function (DCF) model was proposed to depict the spatial relationship between urban land density and PM 2.5 concentrations, quantifying the variation in the relationship from the urban center to the outskirts. The correlation between urban compactness and PM 2.5 concentrations was negative from 2000 to 2010 from the spatial process perspective, showing a gradual weakening; the correlation was reversed from 2010 to 2020. Furthermore, we described the formation and mechanisms of these relationships. These relationships can lead to environmental policies to improve air quality. • Relationships between urban form and PM 2.5 in spatial pattern and process perspective. • Urban compactness and PM2.5 concentrations show a Kuznets inverted U function. • PM 2.5 concentrations increase with urban land density in cubic function shape. • Spatial relationship between urban form and PM 2.5 negative transition to positive. • These relationships can assist in urban planning and air quality improvement. [ABSTRACT FROM AUTHOR]

Identifying the main factors influencing PM 2.5 concentrations is critical for the control and management of elevated atmospheric pollution. Although the impact of natural and land use factors on PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 transmission and dispersion in the NRW. The topography and meteorological factors underlie the basic spatial pattern PM 2.5 concentrations in the NRW, among which seasonal meteorological parameters have a more prominent impact on PM 2.5 concentration. Both natural and land use factors exhibit strong non–stationarity in respect of spatial variations in PM 2.5 , and the effect of environmental factors in general on PM 2.5 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 PM 2.5 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. [ABSTRACT FROM AUTHOR]

Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points in space. However, there are numerous PM2.5 sampling and monitoring facilities that rely on data from only representative points, and which cannot measure the data for the whole region of research interest. This provides the motivation for researching the methods of estimation of particulate matter in areas having fewer monitors at a special scale, an approach now attracting considerable academic interest. The aim of this study is to (1) reclassify and particularize the most frequently used approaches for estimating the PM2.5 concentrations covering an entire research region; (2) list improvements to and integrations of traditional methods and their applications; and (3) compare existing approaches to PM2.5 estimation on the basis of accuracy and applicability.

Planned urban form has become an important strategy to improve air quality in urban agglomerations (UAs), especially pollution due to PM 2.5 , but the influencing mechanisms are not yet clear. This study explores the relationship between four metrics of urban form (size, fragmentation, shape, and dispersion) as determined by analysis of remotely sensed images at 30-m resolution and PM 2.5 concentrations in 19 Chinese UAs. The influence of level of urban development and season is examined. Five control variables, including population density, temperature, precipitation, wind speed, and the normalized difference vegetation index (NDVI) are selected for use in multiple linear regression models. Size, fragmentation, and shape of urban form, but not dispersion, were found to have significant effects on PM 2.5 concentrations of different urbanization-level UAs. Urban size and fragmentation have stronger impacts on PM 2.5 concentrations in UAs with lower urbanization levels while urban shape has a greater impact in higher-level UAs. In terms of seasonal variation in all UAs, urban form is more pronouncedly associated with PM 2.5 concentrations during spring and autumn than summer and winter. Urban size and fragmentation are positively associated with PM 2.5 concentrations whereas urban shape and dispersion are on the contrary. The relationships between urban form and PM 2.5 uncovered here underscore the importance of urban planning as a tool to minimize PM 2.5 pollution. Specifically, local government should encourage polycentric urban form with lower fragmentation in urban agglomerations. UAs with lower urbanization levels should control the disordered expansion of construction land and higher-level UAs should promote the mix of green land and construction land. Moreover, measures to control air pollution from anthropogenic activities in spring, autumn and winter are likely to be more effective in decreasing PM 2.5 concentrations in UAs. • The urban form was measured by urban size, fragmentation, shape, and dispersion. • Effect of urban form on PM 2.5 concentrations varies with urbanization levels of urban agglomerations. • Seasonally, urban form has a stronger effect on PM 2.5 concentrations in spring and autumn. • Polycentric urban form with lower fragmentation should be encouraged in urban agglomerations for reducing PM 2.5 pollution. [ABSTRACT FROM AUTHOR]

• A grey multivariate model with dynamic sinusoidal term is proposed. • The Moore-Penrose generalized inverse matrix is used in the least square method. • The validation set is introduced to the training process of model parameters. • The relationship between sample size and model accuracy is discussed. To solve the problem that the grey multivariate prediction model cannot well simulate systems with periodic oscillations, a novel multivariate grey model called the GM(1,N|sin) power model is proposed. The power exponential term and dynamic sinusoidal function are developed to represent the nonlinear relationship and periodic oscillations of the independent and dependent variables in the proposed model, respectively. First, the discrete time response formula for generating the simulated values at each time point is given. Second, the nonlinear programming model based on the particle swarm optimization algorithm is established to solve the power exponential and periodic coefficients. In addition, to enhance the generalization ability of the parameters, an improved nonlinear programming model considering the accuracy of the validation set is constructed. Finally, in the case studies, the quarterly electricity consumption of Jiangsu Province and PM 2.5 concentrations in Nanjing are adopted to test the effectiveness of this model, and the results are obtained respectively through the GM(1,N|sin) power model and alternative models. The results indicate that the accuracy of GM(1,N|sin) power model in the time series with periodic oscillations outperforms the other six models in this paper. [ABSTRACT FROM AUTHOR]

The prediction of air pollution plays an important role in reducing the emission of air pollutants and guiding people to carry out early warning and control, so it attracts many scholars to conduct modeling and research on it. However, most of the current researches fail to quantify the uncertainty in prediction and only use traditional fuzzy information granulation to process data, resulting in the loss of much detail information. Therefore, this paper proposes a hybrid model based on decomposition and granular fuzzy information to solve these problems. The trend item and the Granulation fluctuation item are respectively predicted and the results are combined to obtain the change trend and fluctuation range of the sequence. This paper selects PM 2.5 concentrations of 3 cities. The experimental results show that the evaluation index of the prediction model is significantly lower than other benchmark models, and a variety of statistical methods are used to further verify the effectiveness of the prediction model. • Quantify the uncertainty of data to obtain a more complete physical meaning. • Fuzzy theory strategies more scientifically measure data characteristics. • The combination of trend term and fluctuation term process data more rationally. • The modified optimization algorithm expands the application scope of the model. [ABSTRACT FROM AUTHOR]

Although a many studies concerning crop residue burning have been conducted, the influence of crop residue burning on local PM2.5 concentrations remains unclear. The number of crop residue burning spots was the highest in Heilongjiang province and we extracted crop residue burning spots for this region using MOD14A1 (Thermal Anomalies & Fire Daily L3 Global 1 km) data and national land cover data. By analyzing the temporal variation of crop residue burning and PM2.5 concentrations in Heilongjiang province, we found that the total number of crop residue burning spots was not correlated with the variations of PM2.5 concentrations at a provincial (regional) scale. However, crop residue burning exerted notable influence on the variations of PM2.5 concentrations at a local scale. We experimented with a set of buffer zone radiuses to examine the influencing area of crop residue burning. The results suggest that the valid influencing area of crop residue burning was between 50 and 80 km. The mean PM2.5 concentration measured at stations close to crop residue burning spots was more than 60 μg/m3 higher than that measured at stations not close to crop residue burning spots. However, no consistent, significant correlation existed between the existence of crop residue burning spots and local PM2.5 concentrations, indicating that local PM2.5 concentrations were influenced by a diversity of factors and not solely controlled by crop residue burning. This research also provides suggestions for better understanding the role of crop residue burning in local and regional air pollution.

As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA–GAM) was proposed to estimate PM2.5 concentrations in this study. The reliability of PCA–GAM for estimating PM2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA–GAM outperforms traditional LUR modelling with relatively higher adjusted R2 (0.94) and lower RMSE (4.08 µg/m3). The CV-adjusted R2 (0.92) is high and close to the model-adjusted R2, proving the robustness of the PCA–GAM model. The PCA–GAM model enhances PM2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA–GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution.