Characteristics and sources of carbonaceous aerosol across urban and rural sites in a rapidly urbanized but low-level industrialized city in the Sichuan Basin, China

Yang, Wei; Xie, Shaodong; Zhang, Ziquan; Hu, Jian; Zhang, Lingyun; Xiong, Li; Zhong, Lijian; Hao, Yufang; Shi, Fangtian

doi:10.1007/s11356-019-05242-7

Cited by 17 publications

(5 citation statements)

References 71 publications

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“…42,43 For instance, PM 2.5 in urban areas has larger contributions from traffic emission and coal combustion, whereas PM 2.5 in rural areas has larger contributions from biomass burning and road dust. 43,44 Different sources lead to disparity in the PM 2.5 composition, and these sources might contribute to the urban and rural disparity in CKD risk associated with PM 2.5 exposure. The findings suggest the potential PM 2.5 effects on CKD risk may differ between urban and Model 2 adjustment (as illustrated in the Table 2 footnote) was used for the stratified analyses.…”

Section: Discussionmentioning

confidence: 99%

Long-Term Exposure to Ambient PM2.5 and Increased Risk of CKD Prevalence in China

Huang

Wang

et al. 2020

JASN

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BackgroundFine particulate matter (PM2.5) is an important environmental risk factor for cardiopulmonary diseases. However, the association between PM2.5 and risk of CKD remains under-recognized, especially in regions with high levels of PM2.5, such as China.MethodsTo explore the association between long-term exposure to ambient PM2.5 and CKD prevalence in China, we used data from the China National Survey of CKD, which included a representative sample of 47,204 adults. We estimated annual exposure to PM2.5 before the survey date at each participant’s address, using a validated, satellite-based, spatiotemporal model with a 10 km×10 km resolution. Participants with eGFR <60 ml/min per 1.73 m2 or albuminuria were defined as having CKD. We used a logistic regression model to estimate the association and analyzed the influence of potential modifiers.ResultsThe 2-year mean PM2.5 concentration was 57.4 μg/m3, with a range from 31.3 to 87.5 μg/m3. An increase of 10 μg/m3 in PM2.5 was positively associated with CKD prevalence (odds ratio [OR], 1.28; 95% confidence interval [CI], 1.22 to 1.35) and albuminuria (OR, 1.39; 95% CI, 1.32 to 1.47). Effect modification indicated these associations were significantly stronger in urban areas compared with rural areas, in males compared with females, in participants aged <65 years compared with participants aged ≥65 years, and in participants without comorbid diseases compared with those with comorbidities.ConclusionsThese findings regarding the relationship between long-term exposure to high ambient PM2.5 levels and CKD in the general Chinese population provide important evidence for policy makers and public health practices to reduce the CKD risk posed by this pollutant.

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Section: Discussionmentioning

confidence: 99%

Long-Term Exposure to Ambient PM2.5 and Increased Risk of CKD Prevalence in China

Huang

Wang

et al. 2020

JASN

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“…Previous studies have clarified the source and compositional differences between rural and urban PM 2.5 . For instance, PM 2.5 in urban areas has larger contributions from traffic emissions and coal combustion, whereas that in rural areas has larger contributions from biomass burning and road dust. , Therefore, elucidating the causal relationship between PM 2.5 constituents and osteoarthritis could provide critical theoretical data for formulating energy structure adjustment policies that reduce the risk of osteoarthritis.…”

Section: Discussionmentioning

confidence: 99%

Long- and Intermediate-Term Ambient Particulate Pollution Is Associated with Increased Osteoarthritis Risk: A Population-Based Prospective Analysis

Wang,

Duan,

et al. 2024

Environ. Sci. Technol.

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Recent studies found the intrusion and retention of exogenous fine particles into joints, but epidemiological data for long- and intermediate-term exposure associations are scare. Here, all urban working, retired employee, and rural residents (16.78 million) in Beijing from January 1, 2011 to December 31, 2019 were included to investigate the effects of long- and intermediate-term ambient particulate exposure on development of osteoarthritis. We identified 1,742,067 participants as first-visit patients with osteoarthritis. For each interquartile range increase in annual PM2.5 (23.32 μg/m3) and PM10 (23.92 μg/m3) exposure concentration, the pooled hazard ratios were respectively 1.238 (95% CI: 1.228, 1.249) and 1.178 (95% CI: 1.168, 1.189) for first osteoarthritis outpatient visits. Moreover, age at first osteoarthritis outpatient visits significantly decreased by 4.52 (95% CI: 3.45 to 5.40) days per μg/m3 for annual PM2.5 exposure at below 67.85 μg/m3. Finally, among the six constituents analyzed, black carbon appears to be the most important component associated with the association between PM2.5 exposure and the three osteoarthritis-related outcomes.

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“…Higher annual mean BC concentrations are mainly distributed over Beijing-Tianjin-Hebei, Shanxi Province, Northeast China, and Central China, which contain many coal mining and combustion activities [57]. At the same time, higher BC values in Yunnan Province and Sichuan Basin would be related to biomass-burning activities [58,59]. The average estimated BC concentration based on the RF model was 2.71 ± 1.18 μgm −3 .…”

Section: Bc Spatial Distributionmentioning

confidence: 97%

Emission-Based Machine Learning Approach for Large-Scale Estimates of Black Carbon in China

Li,

Liu,

Bashiri Khuzestani

et al. 2024

Remote Sensing

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Tremendous efforts have been made to construct large-scale estimates of aerosol components. However, Black Carbon (BC) estimates over large spatiotemporal scales are still limited. We proposed a novel approach utilizing machine-learning techniques to estimate BC on a large scale. We leveraged a comprehensive gridded BC emission database and auxiliary variables as inputs to train various machine learning (ML) models, specifically a Random Forest (RF) algorithm, to estimate high spatiotemporal BC concentration over China. Different ML algorithms have been applied to a large number of potential datasets and detailed variable importance and sensitivity analysis have also been carried out to explore the physical relevance of variables on the BC estimation model. RF algorithm showed the best performance compared with other ML models. Good predictive performance was observed for the training cases (R2 = 0.78, RMSE = 1.37 μgm−3) and test case databases (R2 = 0.77, RMSE = 1.35 μgm−3) on a daily time scale, illustrating a significant improvement compared to previous studies with remote sensing and chemical transport models. The seasonal variation of BC distributions was also evaluated, with the best performance observed in spring and summer (R2 ≈ 0.7–0.76, RMSE ≈ 0.98–1.26 μgm−3), followed by autumn and winter (R2 ≈ 0.7–0.72, RMSE ≈ 1.37–1.63 μgm−3). Variable importance and sensitivity analysis illustrated that the BC emission inventories and meteorology showed the highest importance in estimating BC concentration (R2 = 0.73, RMSE = 1.88 μgm−3). At the same time, albedo data and some land cover type variables were also helpful in improving the model performance. We demonstrated that the emission-based ML model with an appropriate auxiliary database (e.g., satellite and reanalysis datasets) could effectively estimate the spatiotemporal BC concentrations at a large scale. In addition, the promising results obtained through this approach highlight its potential to be utilized for the assessment of other primary pollutants in the future.

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Characteristics and sources of carbonaceous aerosol across urban and rural sites in a rapidly urbanized but low-level industrialized city in the Sichuan Basin, China

Cited by 17 publications

References 71 publications

Long-Term Exposure to Ambient PM2.5 and Increased Risk of CKD Prevalence in China

Long-Term Exposure to Ambient PM2.5 and Increased Risk of CKD Prevalence in China

Long- and Intermediate-Term Ambient Particulate Pollution Is Associated with Increased Osteoarthritis Risk: A Population-Based Prospective Analysis

Emission-Based Machine Learning Approach for Large-Scale Estimates of Black Carbon in China

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