Historical reconstruction of glacier mass balance and its contribution to water resources in the Sawir Mountains from 2000 to 2020.

Glacial changes are crucial to regional water resources and ecosystems in the Sawir Mountains. However, glacial changes, including the mass balance and glacial meltwater of the Sawir Mountains, have sparsely been reported. Three model calibration strategies were constructed including a regression model based on albedo and in-situ mass balance of Muz Taw Glacier (A-Ms), regression model based on albedo and geodetic mass balance of valley, cirque, and hanging glaciers (A-Mr), and degree-day model (DDM) to obtain a reliable glacier mass balance in the Sawir Mountains and provide the latest understanding in the contribution of glacial meltwater runoff to regional water resources. The results indicated that the glacial albedo reduction was significant from 2000 to 2020 for the entire Sawir Mountains, with a rate of 0.015 (10a)-1, and the spatial pattern was higher in the east compared to the west. Second, the three strategies all indicated that the glacier mass balance has been continuously negative during the past 20 periods, and the average annual glacier mass balance was -1.01 m w.e. Third, the average annual glacial meltwater runoff in the Sawir Mountains from 2000 to 2020 was 22 × 106 m3, and its contribution to streamflow was 25.81 % from 2000 to 2018. The glacier contribution rates in the Ulkun- Ulastu, Lhaster, and Kendall River basins were 31.37 %, 22.51 %, and 19.27 %, respectively.

Air pollution in heavy industrial cities along the northern slope of the Tianshan Mountains, Xinjiang: characteristics, meteorological influence, and sources.

The spatiotemporal characteristics, relationship with meteorological factors, and source distribution of air pollutants (January 2017-December 2021) were analyzed to better understand the air pollutants on the northern slope of the Tianshan Mountains (NSTM) in Xinjiang, a heavily polluted urban agglomeration of heavy industries. The results showed that the annual mean concentrations of SO2, NO2, CO, O3, PM2.5, and PM10 were 8.61-13.76 µg m-3, 26.53-36.06 µg m-3, 0.79-1.31 mg m-3, 82.24-87.62 µg m-3, 37.98-51.10 µg m-3, and 84.15-97.47 µg m-3. The concentrations of air pollutants (except O3) showed a decreasing trend. The highest concentrations were in winter, and in Wujiaqu, Shihezi, Changji, Urumqi, and Turpan, the concentrations of particulate matter exceeded the NAAQS Grade II during winter. The west wind and the spread of local pollutants both substantially impacted the high concentrations. According to the analysis of the backward trajectory in winter, the air masses were mainly from eastern Kazakhstan and local emission sources, and PM10 in the airflow had a more significant impact on Turpan; the rest of the cities were more affected by PM2.5. Potential sources included Urumqi-Changj-Shihezi, Turpan, the northern Bayingol Mongolian Autonomous Prefecture, and eastern Kazakhstan. Consequently, the emphasis on improving air quality should be on reducing local emissions, strengthening regional cooperation, and researching transboundary transport of air pollutants.

Urban air quality in Xinjiang and snow chemistry of Urumqi Glacier No. 1 during COVID-19's restrictions.

The unprecedented COVID-19 outbreak impacted the world in many aspects. Air pollutants were largely reduced in cities worldwide in 2020. Using samples from two snow pits dug separately in 2019 and 2020 in Urumqi Glacier No. 1 (UG1) in the Xinjiang Uygur Autonomous Region (Xinjiang), China, we measured water-stable isotopes, soluble ions, and black and organic carbon (BC and OC). Both carbon types show no significant variations in the snow-pit profiles dated from 2018 through 2020. The deposition of anthropogenically induced soluble ions (K+, Cl-, SO42-, and NO3-) in the snow decreased to 20-40% of their respective concentrations between 2019 and 2020; however, they increased 2- to fourfold from 2018 to 2019. We studied the daily concentrations of SO2 (2019-2020), NO2 (2015-2020), CO (2019-2020), and PM2.5 (2019-2020) measured in the sixteen major cities and towns across Xinjiang. The variabilities in these air pollutants were supposed to illustrate the air quality in the urban area and represent the change in the source area. The NO2 decreased in response to mobility restrictions imposed by local governments, while SO2, CO, and PM2.5 did not consistently correspond. This difference indicates that the restriction measures primarily affected traffic. The increases in chemical species in the snow from 2018 to 2019 and the subsequent decreases from 2019 to 2020 were consistent with the variations in SO2 and NO2 measured in urban air and estimated by MERRA-2 model. Therefore, the pandemic could possibly have an impact on snow chemistry of the Tien-Shan glaciers via reduced traffic and industrial intensity; more evidence would be obtained from ice cores, tree rings, and other archives in the future.

Volatile organic compounds in a typical petrochemical industrialized valley city of northwest China based on high-resolution PTR-MS measurements: Characterization, sources and chemical effects.

To scientifically understand the emissions and chemistry of volatile organic compounds (VOCs) in a typical petrochemical industrialized and dust-rich region of Northwest China, VOCs were measured at a receptor site in the Lanzhou Valley using a high-resolution online proton transfer reaction-mass spectrometer (PTR-MS). The ranking of VOC mixing ratios was methanol (32.72 ±â€¯8.94 ppb) > acetaldehyde (5.05 ±â€¯2.4 ppb) > acetic acid (3.42 ±â€¯1.71 ppb). Lanzhou has higher oxygenated VOCs (OVOCs) mixing ratios (methanol and acetaldehyde) and lower aromatics levels (benzene, toluene and C8-aromatics) compared with other cities. The positive matrix factorization (PMF) model showed eight sources of VOCs as follows: (1) mixed industrial process-1 (13.5%), (2) secondary formation (13.2%), (3) mixed industrial process-2 (11.8%), (4) residential biofuel use and waste disposal (13.80%), (5) solvent usage (10.1%), (6) vehicular exhaust (11.8%), (7) biogenic (13.8%) and (8) biomass burning (12.0%). Both the PSCF and the CWT results of mixed industrial process-1 were mainly from the northeast of Lanzhou and the biomass burning was from the southeast; the other four sources (without secondary formation and biogenic) were mainly from the west and northwest of Lanzhou, which were associated with the dust area of the Gobi Desert. A trajectory sector analysis revealed that the local emissions contributed 64.9-71.1% to the VOCs. OVOCs accounted for 43% of the ozone production potential (OFP), and residential biofuel use and waste disposal (25.1%), mixed industrial process-2 (15.3%) and solvent usage (13.4%) appeared to be the dominant sources contributors to O3 production. The rank of main secondary organic aerosols (SOA) precursors under low-NOx conditions is xylene > toluene > benzene > naphthalene > styrene > C10-aromatics > isoprene, while under high-NOx conditions, it is toluene > naphthalene > xylene > C10-aromatics > styrene > benzene > isoprene. Solvent usage and vehicular exhaust appeared to be the dominant contributors to SOA formation.