Search

To explore the contributions of chemical components in the particulate matter in Lhasa, the size distribution characteristics of the chemical components, such as carbonaceous species (organic carbon, OC; elemental carbon, EC), water-soluble ions (NH4+, Cl−, SO42−, and NO3−) and trace elements (Na, Ca, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, K, Mg, and Pb), were investigated from August 2018 to May 2019. Principal component analysis (PCA) was also used to simulate the source of the particulate matter. The chemical components showed bimodal size distributions, except for Cu. The highest mass concentration of OC appeared at < 0.49 μm, and the second highest one existed at 1.5–3.0 μm in winter or at 3.0–7.2 μm in other seasons. The maximum concentrations of NH4+, Cl−, SO42−, and NO3− were at < 0.49 μm and peaked at 0.95–1.5 μm or > 3.0 μm. For seasonality, the concentrations of NO3− and SO42− were considerably higher in summer and autumn; trace elements (except for Cu, Zn, V, and Ni), OC, and EC presented higher values during late autumn and winter. The ratio between OC and EC (4.15–33.80) indicated the existence of secondary pollution in Lhasa. The [NO3−]–[SO42−] ratios during summer and autumn exceeding 1 suggested that the currently predominant vehicle exhaust made a greater contribution to the aerosols. According to the results of a PCA, the main pollution sources of particulate matter in Lhasa were suspended dust, biomass burning, fossil fuel combustion, secondary pollution, and vehicular emissions. [ABSTRACT FROM AUTHOR]

In order to realize low-carbon and high-quality development, this study took the carbon emissions of each district and county in the Chengdu–Chongqing region from 2005 to 2017 as the research object and used the spatial autocorrelation model to analyze the spatial and temporal evolution characteristics of carbon emissions in the counties of the Chengdu–Chongqing region, so as to fill in the research blank of carbon emissions in the counties of the Chengdu–Chongqing region. Then, the geographical detector model is used to explore the interaction among influencing factors of carbon emissions and reveal the time changes and regional differences of influencing factors, so as to improve the lack of spatial and temporal heterogeneity of influencing factors of carbon emissions by geographical detector. The results show the following: (i) The overall carbon emissions of counties show a year-on-year growth trend with the main urban areas of Chengdu and Chongqing as the core, but the growth rate slows down after 2010. (ii) The carbon emissions showed a significant positive spatial autocorrelation, and the neighboring counties showed a spatial clustering characteristic of "high-high" or "low-low", and the clustering status tended to be enhanced. (iii) The carbon emissions are strongly influenced by industrial structure, economic development, investment level, financial situation, urbanization rate and social consumption, and their interactions are all enhanced, but the influence mostly tends to rise first and then fall. (iv) County carbon emissions can be divided into four types of geographical types, such as population size influencing type, urbanization rate influencing type, economic development influencing type and industrial structure influencing type. Therefore, a variety of factors need to be considered comprehensively, a multi-pronged approach, and a comprehensive policy to realize low-carbon transformation in the Chengdu–Chongqing region. [ABSTRACT FROM AUTHOR]