Spatiotemporal variations of NO2 and its driving factors in the coastal ports of China

Zhang, Yang; Shi, Meiyu; Chen, Jihong; Fu, Shanshan; Wang, Huizhen

doi:10.1016/j.scitotenv.2023.162041

Cited by 14 publications

(5 citation statements)

References 79 publications

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“…By creating local regression equations at each location within the spatial range, GWR analyzes the spatial changes and associated driving factors of the study area at a specific scale. Compared to ordinary panel regression, which does not consider the spatial distance factor, GWR can more accurately test the spatial heterogeneity relationship between independent and dependent variables [43]. In the present study, the GWR model was used to examine the geographical differentiation characteristics of O 3 concentration in different provinces of China based on meteorological, natural, and socioeconomic factors.…”

Section: Geographically and Temporally Weighted Regression (Gtwr)mentioning

confidence: 99%

Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China

Zhou

Liu

2023

Sustainability

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Tropospheric ozone (O3) pollution has emerged as a significant concern, as it can adversely influence human health, daily activities, and the surrounding environment(The following tropospheric O3 is referred to as O3). Research on the societal contribution to O3 primarily concentrates on the generation mechanisms and chemical processes, with limited studies examining the influence of social and economic activities on O3 at a national scale. In this investigation, spatial econometric models, random forest models, and geographically weighted regression (GWR) were adopted for assessing the effects of meteorological, natural, and socioeconomic factors on O3 concentration throughout the country. The spatial error model (SEM) revealed that precipitation, temperature, wind direction, per capita GDP, RD project funding, and SO2 were the primary factors influencing O3 concentration in China, among which precipitation had the strongest effect on O3, followed by temperature and SO2. Subsequently, the GWR model was utilized to demonstrate the regional differences in the impacts of precipitation, NOx, secondary industry proportion, and electricity consumption. In central and western regions, such as Jiangxi, Guangxi, and Guizhou, precipitation, NOx, and power consumption were the leading factors contributing to severe O3 pollution. The secondary industry proportion substantially affected O3 pollution in the Beijing-Tianjin-Hebei region, indicating that this sector played a crucial role in the region’s economic growth and contributed to elevated O3 concentrations. Meteorological, natural, and socioeconomic factors exhibited a lesser influence on O3 pollution in most eastern regions compared to central and western regions. This study’s findings identified the primary contributors to O3 pollution and provided a scientific basis for developing strategies to mitigate its impact.

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Section: Geographically and Temporally Weighted Regression (Gtwr)mentioning

confidence: 99%

Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China

Zhou

Liu

2023

Sustainability

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“…NO 2 mainly comes from stationary (burning) and mobile sources (vehicle exhaust) [132][133][134]. While, SO 2 is a traditional industrial pollutant mainly derived from stationary sources such as coal combustion, power generation and industrial production [135][136][137][138][139].…”

Section: No 2 /So 2 Ratiomentioning

confidence: 99%

Variation characteristics and coordinated emission reduction of air pollutants in megacity of Chengdu-Chongqing economic circle under dual carbon goal

Li,

Abdullah,

Sobri

et al. 2023

Environmental Engineering Research

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The air pollution process and impact mechanism of megacities located in complex terrain are particularly complex. As a typical megacity in China, Chengdu has unique topographical and meteorological conditions, and its pollution control is difficult. This study systematically elucidated the variation characteristics of six criteria air pollutants in Chengdu between 2014 and 2020. Besides, the PM2.5/PM10 and NO2/SO2 ratios were discussed. Furthermore, a detailed analysis of the correlation between air pollutants was carried out. Finally, the collaborative path of carbon reduction and air pollution control is discussed. The results indicated that SO2, PM2.5, PM10 and CO were significantly decreased by 62.9%, 50.8%, 45.5%, and 36.7%, respectively. PM2.5 and O3 compliance rates are very low, and O3 increases with fluctuations. SO2, NO2, CO, PM showed a “U-shaped” seasonal variation, and there was a “seesaw” phenomenon between O3 and PM2.5. The continuous changing trends also found in the ratios of PM2.5/PM10 and NO2/SO2. The results highlight the importance of coordinated reduction of carbon emissions and pollutants in Chengdu. This research can improve the prediction accuracy of air pollution in complex terrain areas under global warming, and improve the understanding of the formation mechanism of air pollution in special terrains around the world.

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“…Among them, geodetector and GWR, as commonly used spatial analysis models, take into account the spatial heterogeneity of variable relationships, and are better able to express complex geographic change processes compared to linear regression methods [23,25]. But they can only investigate the drivers for a single time period [26,27]. In fact, the driving process of vegetation evolution exhibit both spatiotemporal nonstationarity and time lag [28].…”

Section: Introductionmentioning

confidence: 99%

“…Ignoring the unbalanced effects of the time dimension may affect the accuracy of their results [29,30]. Geographically and Temporally Weighted Regression (GTWR) is an extension of the GWR model, which can reflect the spatial and temporal variability of variables by constructing a weight matrix based on spatial and temporal distances, has received increasing attention [26,31]. For instance, Hu et al used GTWR and MGWR to investigate driving mechanisms of landscape patterns on habitat quality [32].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Change of Urban Green Space and Spatiotemporal Heterogeneity Analysis of Driving Factors

Wang,

Lin,

et al. 2024

Sustainability

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Analyzing the change trend of urban green space (UGS) and exploring related driving forces can provide scientific reference for sustainable development in rapidly urbanizing areas. However, the spatial and temporal driving mechanisms of the drivers on UGS patterns at different scales are still not deeply understood. Based on the GlobeLand30 land cover data, nighttime lighting data and spatial statistics from 2000 to 2020, this study analyzed the size, shape and diversity of UGS in Guangzhou at the urban level, gradient level and township level with multiple landscape indices. Diversity means the richness of UGS patch types. The selected indices include percent of landscape (PLAND), largest path index (LPI), landscape shape index (LSI), aggregation index (AI) and Shannon’s diversity index (SHDI). The spatiotemporal heterogeneity of the drivers was then explored using the spatiotemporal weighted regression (GTWR) method. Results showed the following: (1) During 2000−2020, the total amount of UGS in Guangzhou increased slightly and then decreased gradually. UGS was mainly transferred into artificial surfaces (lands modified by human activities). (2) The UGS landscape showed a non-linear trend along the urban–rural gradient and fluctuated more in the interval of 20–60% urbanization level. PLAND, LPI and AI decreased significantly in areas with higher levels of urbanization. LSI increased and SHDI decreased significantly in areas with lower levels of urbanization. At township level, the landscape indices showed significant spatial autocorrelation. They transformed from discrete changes at the edge and at the junction of the administrative district to large-scale aggregated change, especially in northern areas. (3) The size of UGSs was mainly influenced by natural factors and population density, but their shape and diversity were mainly influenced by socio-economic factors. More regular shapes of green patches were expected in higher urbanization areas. Population agglomeration positively influenced green space patterns in the northeastern and southern regions (Zengcheng, Conghua and Nansha). Meanwhile the negative influence of urban expansion on the green space pattern in the central and southern regions decreased over time. This study contributes to an in-depth understanding of how the key factors affect the different changes of UGS with time and space and provides methodological support for the long-term zoning planning and management of UGS.

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Spatiotemporal variations of NO2 and its driving factors in the coastal ports of China

Cited by 14 publications

References 79 publications

Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China

Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China

Variation characteristics and coordinated emission reduction of air pollutants in megacity of Chengdu-Chongqing economic circle under dual carbon goal

Multilevel Change of Urban Green Space and Spatiotemporal Heterogeneity Analysis of Driving Factors

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