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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (4): 584-607    DOI: 10.1016/j.jaridl.2026.04.003     CSTR: 32276.14.JAL.20250562
Research article     
Precipitation or temperature? Nonlinear responses of particulate matter and ozone to meteorological extremes in an arid climate
LI Yalong1,2,3,4,5,6, HU Bing1,2, Marie Anne Eurie FORIO3, CHANG Cun1,7, QIAO Xuning8,9, NAIBI Sulei1,2,3,5,6, LI Tao1,2,3,5,6, SONG Fengjiao1,2,3,5,6, YANG Bin1,2, LIU Hailong1,10, BAO Anming1,7,11,12,13,*(), Peter GOETHALS3
1 State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2 University of Chinese Academy of Sciences, Beijing 100049, China
3 Department of Animal Sciences and Aquatic Ecology, Ghent University, Ghent 9000, Belgium
4 Tarim University, Alaer 843300, China
5 Sino-Belgian Joint Laboratory of Geo-Information, Urumqi 830011, China
6 Sino-Belgian Joint Laboratory of Geo-Information, Ghent 9000, Belgium
7 Key Laboratory of Geographic Information System (GIS) & Remote Sensing (RS) Application Xinjiang Uygur Autonomous Region, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
8 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
9 Research Centre of Arable Land Protection and Urban-Rural High-Quality Development of Yellow River Basin, Henan Polytechnic University, Jiaozuo 454003, China
10 University of Electronic Science and Technology of China, Chengdu 611731, China
11 Sino-Belgian Joint Laboratory for Geo-Information, Urumqi 830011, China
12 China-Pakistan Joint Research Center on Earth Sciences, Chinese Academy of Sciences-Higher Education Commission of Pakistan (CAS-HEC), Islamabad 45320, Pakistan
13 Qinghai Forestry Carbon Sequestration Service Center, Xining 810001, China
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Abstract  

Northern Xinjiang, an arid inland area in Northwest China, is highly vulnerable to air pollution under intensifying climate extremes, yet the relative roles of temperature and precipitation extremes remain insufficiently understood. Using multi-source datasets for 2000-2023, including China High Air Pollutants (CHAP) particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and ozone (O3) products and Expert Team on Climate Change Detection and Indices (ETCCDI) extreme climate indices derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5)-Land, together with trend detection, change-point analysis, pixel-wise Pearson correlation, and random forest (RF) modeling, we investigated the spatiotemporal evolution of major air pollutants and their responses to meteorological extremes in northern Xinjiang. PM2.5 and PM10 generally declined from 2000 to 2023, whereas O3 increased, indicating a shift from particulate-dominated pollution toward stronger photochemical pollution. Interannually, PM2.5 showed a rise-decline pattern, PM10 exhibited a rise-decline-rebound pattern, and O3 increased markedly after 2015. Clear seasonal contrasts were observed, with PM2.5 peaking in winter, PM10 in spring, and O3 in summer. During the same period, northern Xinjiang exhibited a pronounced warming-drying tendency, characterized by increasing heat-related indices, decreasing cold-related indices, reduced precipitation totals and heavy-rainfall frequency, and increasing consecutive dry days. Pollutant-climate relationships showed strong spatial heterogeneity and pollutant-specific contrasts across the Urumqi-Changji-Shihezi corridor, the Ili River Valley, and the Junggar Basin. PM2.5 responses to precipitation shifted from predominantly positive to negative, PM10 showed mainly negative associations with precipitation extremes, and O3 responses varied by subregion. Temperature-related extremes generally explained more pollutant variability than precipitation-related extremes, with PM2.5 showing the highest sensitivity. These findings highlight the coupled influences of warming, drying, emissions, and terrain-controlled transport on air quality and support region-specific, multi-pollutant strategies for coordinated climate adaptation and air pollution control in northern Xinjiang.

Key words： extreme climate events      air pollution dynamics      climate-pollution coupling      ozone formation mechanisms      gas-particle transformation      random forest     
Received: 10 November 2025      Published: 30 April 2026
Corresponding Authors: *BAO Anming (E-mail: baoam@ms.xjb.ac.cn)
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LI Yalong
HU Bing
Marie Anne Eurie FORIO
CHANG Cun
QIAO Xuning
NAIBI Sulei
LI Tao
SONG Fengjiao
YANG Bin
LIU Hailong
BAO Anming
Peter GOETHALS
Cite this article:

LI Yalong, HU Bing, Marie Anne Eurie FORIO, CHANG Cun, QIAO Xuning, NAIBI Sulei, LI Tao, SONG Fengjiao, YANG Bin, LIU Hailong, BAO Anming, Peter GOETHALS. Precipitation or temperature? Nonlinear responses of particulate matter and ozone to meteorological extremes in an arid climate. Journal of Arid Land, 2026, 18(4): 584-607.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.04.003     OR     http://jal.xjegi.com/Y2026/V18/I4/584

Fig. 1 Elevation (a) and remote sensing image (b) of northern Xinjiang. DEM, digital elevation model.
Classification ID Indicator name Definition Unit
Extreme temperature FD Frost days Annual count when TN<0.0°C d
SU Summer days Annual count when TX>25.0°C d
ID Ice days Annual count when TX<0.0°C d
TR Tropical nights Annual count when TN>20.0°C d
GSL Growing season length Annual (1st January to 31st December in NH or 1st July to 30th June of next year in SH) count between first span of at least 6 d with TG>5.0°C and first span after 1st July (1st January in SH) of 6 d with TG<5.0°C d
TX10P Cool days Monthly count of days when TX<10th percentile d
TX90P Warm days Monthly count of days when TX>90th percentile d
TN10P Cool nights Monthly count of days when TN<10th percentile d
TN90P Warm nights Monthly count of days when TN>90th percentile d
CSDI Cold spell duration indicator Annual count of days with at least 6 consecutive days when TN<10th percentile d
WSDI Warm spell duration indicator Annual count of days with at least 6 consecutive days when TX>90th percentile d
DTR Diurnal temperature range Monthly mean difference between TX and TN ℃
Extreme precipitation RX1day Maximum 1-day precipitation amount Monthly maximum 1-day precipitation mm
RX5day Maximum 5-day precipitation amount Monthly maximum consecutive 5-day precipitation mm
R95P Very wet days Annual total PRCP when RR>95th percentile mm
R99P Extremely wet days Annual total PRCP when RR>99th percentile mm
CDD Consecutive dry days Maximum number of consecutive days with RR<1.0 mm d
CWD Consecutive wet days Maximum number of consecutive days with RR≥1.0 mm d
SDII Simple daily intensity index Annual total precipitation divided by the number of wet days (defined as PRCP≥1.0 mm) in the year mm/d
R5MM Number of moderate precipitation days Annual count of days when PRCP≥5.0 mm d
R10MM Number of heavy precipitation days Annual count of days when PRCP≥10.0 mm d
PRCPTOT Annual total wet day precipitation Annual total PRCP in wet days (RR≥1.0 mm) mm
Table 1 List of the Expert Team on Climate Change Detection and Indices (ETCCDI) core climate indices
Fig. 2 Spatial distribution of annual mean particulate matter 2.5 (PM2.5) concentrations in northern Xinjiang from 2000 to 2023. (a), 2000; (b), 2001; (c), 2002; (d), 2003; (e), 2004; (f), 2005; (g), 2006; (h), 2007; (i), 2008; (j), 2009; (k), 2010; (l), 2011; (m), 2012; (n), 2013; (o), 2014; (p), 2015; (q), 2016; (r), 2017; (s), 2018; (t), 2019; (u), 2020; (v), 2021; (w), 2022; (x), 2023. White areas indicate missing data and were excluded from analysis.
Fig. 3 Spatial distribution of annual mean particulate matter 10 (PM10) concentrations in northern Xinjiang from 2000 to 2023. (a), 2000; (b), 2001; (c), 2002; (d), 2003; (e), 2004; (f), 2005; (g), 2006; (h), 2007; (i), 2008; (j), 2009; (k), 2010; (l), 2011; (m), 2012; (n), 2013; (o), 2014; (p), 2015; (q), 2016; (r), 2017; (s), 2018; (t), 2019; (u), 2020; (v), 2021; (w), 2022; (x), 2023. White areas indicate missing data and were excluded from analysis.
Fig. 4 Spatial distribution of annual mean ozone (O3) concentrations in northern Xinjiang from 2000 to 2023. (a), 2000; (b), 2001; (c), 2002; (d), 2003; (e), 2004; (f), 2005; (g), 2006; (h), 2007; (i), 2008; (j), 2009; (k), 2010; (l), 2011; (m), 2012; (n), 2013; (o), 2014; (p), 2015; (q), 2016; (r), 2017; (s), 2018; (t), 2019; (u), 2020; (v), 2021; (w), 2022; (x), 2023. White areas indicate missing data and were excluded from analysis.
Fig. 5 Spatial distribution of Theil-Sen slope (a-c) and Mann-Kendall (M-K) significance (d-f) for PM2.5, PM10, and O3 in northern Xinjiang from 2000 to 2023
Fig. 6 Interannual (a1-a3 and b1-b3), seasonal (c1-c3), and monthly (d) variation characteristics of PM2.5, PM10, and O3 in northern Xinjiang from 2000 to 2023. In panels b1-b3, UF and UB represent the forward and backward sequential statistics of the M-K mutation test, respectively; and in panels c1-c3, IQR represents the interquartile range.
Fig. 7 Linear trends of extreme precipitation indices. (a), consecutive dry days (CDD); (b), consecutive wet days (CWD); (c), annual total wet day precipitation (PRCPTOT); (d), number of moderate precipitation days (R5MM); (e), number of heavy precipitation days (R10MM); (f), very wet days (R95P); (g), extremely wet days (R99P); (h), maximum 1-day precipitation amount (RX1day); (i), maximum 5-day precipitation amount (RX5day); (j), simple daily intensity index (SDII).
Fig. 8 Spatial distribution of mean values (a1-a10), Theil-Sen slope (b1-b10), and M-K significance test results (c1-c10) of extreme precipitation indices
Fig. 9 Linear trends of extreme temperature indices. (a), cold spell duration indicator (CSDI); (b), diurnal temperature range (DTR); (c), frost days (FD); (d), growing season length (GSL); (e), ice days (ID); (f), summer days (SU); (g), cool nights (TN10P); (h), warm nights (TN90P); (i), tropical nights (TR); (j), cool days (TX10P); (k), warm days (TX90P); (l), warm spell duration indicator (WSDI).
Fig. 10 Spatial distribution of mean values (a1-a12), Theil-Sen slope (b1-b12), and M-K significance test results (c1-c12) of extreme temperature indices
Fig. 11 Atmospheric pollution-extreme precipitation index spatial correlation patterns. (a1-a10), PM2.5; (b1-b10), PM10; (c1-c10), O3. Pixel-wise Pearson correlation coefficients (r) are computed from annual time series (2000-2023; N=24). Statistical significance can be assessed using a two-sided t-test for Pearson's r at P<0.05 level; thus, weak correlations should be interpreted cautiously.
Fig. 12 Atmospheric pollution-extreme temperature index spatial correlation patterns. (a1-a12), PM2.5; (b1-b12), PM10; (c1-c12), O3. Pixel-wise Pearson correlation coefficients (r) are computed from annual time series (2000-2023; N=24). Statistical significance can be assessed using a two-sided t-test for Pearson's r at P<0.05 level; thus, weak correlations should be interpreted cautiously.
Fig. 13 Heatmap of correlations between air pollutants and extreme climate indices
Fig. 14 Random forest (RF) importance of air pollutants in relation to extreme climate indices in northern Xinjiang from 2000 to 2023. (a), 2000; (b), 2005; (c), 2010; (d), 2015; (e), 2020; (f), 2023.
Fig. 15 Schematic diagram of particulate matter sources. SO2, sulfur dioxide; NH3, ammonia; NOₓ, nitrogen oxides; VOCs, volatile organic compounds; UV, ultraviolet radiation; H2SO4, sulfuric acid; HNO3, nitric acid; RO2•, organic peroxy radicals; SOA, secondary organic aerosol; NH4NO3, ammonium nitrate; (NH4)2SO4, ammonium sulfate.
Fig. 16 Sources of O3 and photochemical reaction processes. NO, nitric oxide; O, atomic oxygen; NO2, nitrogen dioxide; O2, molecular oxygen.
Fig. 17 Temperature-precipitation climate envelopes of PM2.5 (a), PM10 (b), and O3 (c) in northern Xinjiang. Scatter points represent 5000 randomly selected point-year samples from pooled annual observations during 2000-2023. Pollutant concentrations were classified into low, medium, and high groups using tertile-based thresholds derived from the pooled multi-year samples, where P33 and P67 denote the 33rd and 67th percentiles of each pollutant distribution, respectively. Marginal histograms indicate the frequency distributions along the temperature and precipitation axes.
Fig. 18 Pollution sources, transformation, and climate impacts. P, precipitation; Pc_R, photochemical reaction; PBLH, planetary boundary layer height; T, temperature; UHI, urban heat island effect.
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