Spatiotemporal variations of atmospheric black carbon concentration and its correlation with meteorological and environmental factors in Xinjiang, China, from 2010 to 2022

Abstract

Black carbon (BC) is a pollutant produced by the combustion of fossil fuels and biomass fuels, which has a huge impact on regional climate, atmospheric environment, and human health. In this study, based on MERRA-2 reanalysis data and ground-based observation data, the Mann-Kendall (MK) test and random forest (RF) model were used to explore the spatiotemporal variation characteristics of atmospheric BC concentration in Xinjiang, China and its correlation with meteorological and environmental covariates in 2010–2022. The results showed that the use of MERRA-2 reanalysis data to explore the spatiotemporal variation characteristics of BC concentration in Xinjiang had high reliability (relative average deviation (RAD) = 0.65). From 2010 to 2022, the annual average concentration of atmospheric BC in Xinjiang was 195.40 ± 15.55 ng/m3, and the multi-year average change rate was −0.05%. The winter season had the highest atmospheric BC concentration (145.52 ± 39.31 ng/m3), followed by autumn (124.95 ± 28.82 ng/m3), spring (74.05 ± 9.96 ng/m3), and summer (73.41 ± 5.69 ng/m3). The atmospheric BC concentration had a significant spatial variation, showing two high-BC-concentration areas on the northern slope of the Tianshan Mountains (centered on Urumqi-Changji-Shihezi region) and the urban agglomeration around the Tarim Basin (centered on Kashgar). The RF model analysis showed that meteorological factors including snow depth, surface temperature, and humidity as well as environmental factors including NO2, PM10, and SO2 were the main factors affecting the BC concentration. This work is of great significance for clarifying the accumulation and spatial distribution characteristics of atmospheric BC in northwest China and the factors influencing the atmospheric BC concentration, and helps to raise public attention to the increasingly serious climate change and public health problems caused by BC.

Keywords

• M-K trend test
• Random forest model
• Northwest China
• Air pollutants