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As the negative repercussions of environmental devastation, such as air quality decline and air pollution, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. China pledged to achieve air quality improvement goal to combat global environment issue, yet the spatial-temporal differentiation and its driving factors of environment-meteorology-economic index for air quality are not fully analysed. To promote regional collaborative control of air pollution and achieve sustainable urban development, spatial and temporal different and its driving factors of air quality in Shandong Province during 2013–2020. Results revealed that concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and carbon monoxide (CO-95per) exhibited decreasing trend (SO2 concentrations decreasing 84 % and CO-95per concentrations decreasing 90 %). Air quality was improved from inland areas to coastal areas. Pollutant indicators of SO2, NO2, PM10, PM2.5, and CO-95per demonstrated significant positive correlation (P < 0.05). Air temperature and precipitation are significantly negatively correlated with concentrations of SO2, NO2, PM10, PM2.5, and CO-95per but significantly positively correlated with ozone (O3-8 h). SO2, NO2, PM2.5, PM10, CO-95per, and proportion of days with heavy pollution are strongly positively correlated with proportion of secondary industry but strongly negatively correlated with proportion of tertiary industry and volume of household waste. Except for O3-8 h, pollutant index of Provincial Capital Economic Circle (PCEC) and Southern Shandong Economic Circle (SSEC) has significant negative correlation (P < 0.05) with regional gross domestic product and investment in environmental protection; however, investment in environmental protection of Eastern Shandong Economic Circle (ESEC) has no significant correlation with air pollution index. There was significant negative correlation between vegetable sowing area and SSEC pollutant index. The relationship between pollution emission and investment in environmental protection has shifted from high pollution-low investment to low pollution-low investment in PCEC, ESEC and SSEC, and the inflection point was in 2020 for PCEC, 2019 for ESEC, and 2020 for SSEC. Those results provide empirical evidence and theoretical support for the improvement of regional air quality, aiming to achieve high-quality development. According to these findings, it has been found that meteorological elements, pollutant emission, socio-economic factors and agricultural data affect air quality. Those results could provide meaningful and significant supporting for synergistic regulation of diverse pollutants.

As land and sea integration continues to accelerate, it will inevitably have an effect on the marine ecological environment. However, the study on spatial and temporal distribution characteristics of Chinese marine environmental quality and its driving factors was lacking. This paper employs Regression Analysis, Spearman Correlation Analysis, Canonical Correlation Analysis, Boston Consulting Group Matrix and Environmental Kuznets Curve to examine the spatial–temporal distribution characteristics and correlation factors of Chinese jurisdictional sea areas environmental quality. The environmental quality of the waters under China's jurisdiction improved on average from 2003 to 2021, the area of sea with excellent water quality increased by 7.21 × 104 km2. The spatial distribution showed “South superior, North inferior” trend. The accumulated red tide area is 2.01 × 105 km2, and the green tide has the average maximum area of distribution is 3.13 × 104 km2. The marine dumping area's volume of waste has a three stages of slow rise (2003–2007), steady (2008–2014) and rapid rise (2015–2021). The average percentage of seawater bathing areas with good water quality is 82.10 %. Urban Environmental Infrastructure Investment and Urban Drainage Construction Investments are positively correlated with excellent water quality and negatively correlated with light-sewage water quality. The typical correlation for the group “Environmental Quality/Environmental Governance” is 0.911, and the strongest correlation is between excellent water quality and urban drainage construction investment. The typical correlation coefficient for the “Environmental Quality/Socioeconomic” group is 0.960, and the strongest correlation is between excellent water quality and per capita GDP. The Gross Ocean Product, Per Capita GDP, Proportion of Non-agricultural Industries, Year-end Resident Population, and excellent water quality are positively correlated whereas light-sewage water quality is negatively correlated. Liaoning, Tianjin, Shandong and Guangdong shift from high-pollution-low-investment to low-pollution-low-investment model. Shanghai, Zhejiang, Fujian and Hainan maintain high-pollution-low-investment model, Hainan maintains high-pollution-high-investment model. Jiangsu maintains low-pollution-low-investment model. Hebei and Guangxi shift from low-pollution-low-investment to low-pollution-high-investment model. Liaoning, Hebei, Tianjin, Shandong, Guangxi and Shanghai show inverted “N” shape, Jiangsu shows positive “N” shape, Zhejiang and Fujian show monotonic decline shape, Guangdong and Hainan show positive “U” shape. The results could provide valuable suggestions for coastal provinces to accomplish economic development and environmental improvement, which to prevent the old way of “pollution first and treatment later”.