Atmospheric Environment of the Northern Tianshan Urban Agglomeration, Xinjiang: A Comprehensive Review of Pollutant Characteristics, Photochemical Processes, and Health Implications

The Northern Tianshan Urban Agglomeration (NTUA) in Xinjiang, China, represents a distinctive arid region atmospheric environment where mountain–valley meteorology, Central Asian dust transport, and coal-intensive industrial emissions converge. This review provides a structured narrative synthesis of air quality research conducted in the NTUA within an “emission source–photochemical transformation–health burden” framework, covering ozone, particulate matter, carbonaceous aerosols, VOCs, NOx, free radicals, and reactive oxygen species (ROS). Evidence is classified into three categories: direct NTUA measurements (I), regional Central Asian observations (II), and extrapolated evidence from other regions (III). Available data indicate a seasonal transition from dust-dominated coarse-mode aerosol in spring to secondary inorganic and carbonaceous fine-mode dominance in winter. Ozone formation may shift from VOC-limited conditions in winter to NOx-limited photochemistry in summer at suburban sites, although direct regime diagnoses remain limited. Carbonaceous aerosols exhibit contrasting patterns: black carbon is dominated by local fossil fuel combustion, while brown carbon is speculated to shift from primary sources in winter to secondary formation in summer. The atmospheric oxidation capacity remains poorly constrained due to the absence of direct radical measurements. Health risk assessments indicate that the combined exposure to PM2.5, ozone, and dust may result in multi-pollutant effects that cannot be captured by single-pollutant models, although local epidemiological evidence remains limited. Critical gaps include the lack of an integrated observational–modeling framework, direct radical and oxidative potential measurements, and NTUA-specific cohort studies. A tiered research agenda integrating enhanced monitoring, intensive field campaigns, satellite remote sensing, and machine learning is proposed.