Influencing factors and trend prediction of PM2.5 concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China

Abstract

The government’s development of eco-environmental policies can have a scientific foundation thanks to the fine particulate matter (PM 2.5 ) medium- and long-term change forecast. This study develops a STRIPAT-Scenario analysis framework employing panel data from 11 cities in Zhejiang Province between 2006 and 2020 to predict the changing trend of PM 2.5 concentrations under five alternative scenarios. The results reveal that: (1) urbanization development ( P ), economic development ( A ), technological innovation investment ( T ) and environmental regulation intensity have a significant inhibitory effect on PM 2.5 concentration in Zhejiang Province, while industrial structure, industrial energy consumption and the number of motor vehicles ( TR ) have a significant increase on PM 2.5 concentration. (2) Under any scenario, the PM 2.5 concentration of 11 cities in Zhejiang Province can reach the constraint target set in the 14th Five-Year plan. The improvement in urban PM 2.5 quality is most obviously impacted by the high-quality development scenario (S4). (3) Toward 2035, PM 2.5 concentrations of 11 cities in Zhejiang Province can reach the National Class I level standard in most scenario models, among which Hangzhou, Jiaxing and Shaoxing are under high pressure to reduce emissions and are the key areas for PM 2.5 management in Zhejiang Province. However, most cities cannot reach the 10 μg/m 3 limit of WHO’s AQG2005 version. Finally, this study makes recommendations for reducing PM 2.5 in terms of enhancing industrial structure and funding science and technology innovation.

Scientometric and multidimensional contents analysis of PM2.5 concentration prediction

“…(1) Socio-economic indicators: these resources are the fundamental sources of PM 2.5 generation, such as energy consumption, energy structure, industrial structure, population density, urbanization level, and economic development status. Data on land use, landscape index, traffic information, population, regional pollution emissions, three-dimensional building morphology, food service distribution, bus stop density, intersection density, and shortest distance to roads are also included in regression prediction models [ 3 , 67 ]. (2) Natural condition indicators, topographic condition data include: Enhanced Vegetation Index (EVI), Digital elevation model (DEM), and Normalized Vegetation Index (NDVI); Meteorological conditions include: relative humidity (RHU), mean pressure (PRS), mean temperature (TEM), gradient Surface Temperature (GST), mean wind speed (WIN–S), wind direction (WIN-D), visibility (VIS), planetary boundary layer height (PBL), dew point temperature (DPT), atmospheric stability etc.…”

“…The MLR model based on the SRIPAT framework is given in Fig. 7 ②, which explores the long-term PM 2.5 level prediction at the city scale in combination with the scenario prediction method [ 3 ]. In addition, more frequently used is the LUR (in Fig.…”

“…Medium-term scale (monthly and seasonal) forecasts can provide a basis for seasonal production formulation adjustment and pollution prevention and control in the industrial sector. Long-term scale (annual) PM 2.5 concentration prediction can provide a basis for regional environmental planning and national economic development planning target setting [ 3 , 9 ].…”

“…In contrast, urban macro-scale data are often obtained by averaging multi-site data. Therefore, multivariate panel regression analysis models and scenario simulation techniques on annual average concentration analysis are often used to analyze the impact mechanism of PM 2.5 pollution at the socio-economic level and long-term prediction studies [ 3 ].…”

“…The increase in PM 2.5 concentration will not only affect human health and residents’ lifestyles but also bring about climate and environmental changes [ 1 , 2 ]. Consequently, precise PM 2.5 concentration prediction is crucial for preventing and controlling air pollution [ 3 , 4 ].…”