School of Forestry, Northeast Forestry University, Harbin 150040, China.
Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China.
Int J Environ Res Public Health. 2022 Sep 15;19(18):11627. doi: 10.3390/ijerph191811627.
Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial-temporal heterogeneity of PM (PM and PM) concentration in Heilongjiang Province during 2014-2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO, NO, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.
颗粒物 (PM) 会降低空气质量并对人类健康产生负面影响。本研究基于主成分分析-普通最小二乘回归（PCA-OLS）、主成分分析-地理加权回归（PCA-GWR）、主成分分析-时间加权回归（PCA-TWR）和主成分分析-地理和时间加权回归（PCA-GTWR），分析了 2014-2018 年黑龙江省 PM（PM 和 PM）浓度的时空异质性及其关键影响因素。结果表明，六个主成分分别代表温度、风速、气压、大气污染、湿度和植被覆盖因子，分别代表原始变量的 87%。所有局部模型（PCA-GWR、PCA-TWR 和 PCA-GTWR）均优于全局模型（PCA-OLS），而 PCA-GTWR 的性能最佳。由于季节性周期性，PM 的时间异质性大于空间异质性。空气污染物（即 SO、NO 和 CO）和气压促进了 PM 浓度，而温度、风速和植被覆盖则抑制了 PM 浓度。年 PM 浓度的下降趋势明显，尤其是 2017 年以后，热点逐渐从西南部城市向东南部城市转移。本研究通过解决 PM 的过度影响因素（即气象因素、空气污染物、植被覆盖）及其时空异质性，为地方政府的精准防控奠定了基础。
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