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Long-term exposure to ambient particulate pollutants (PM2.5 and PM10) may increase the risk of chronic kidney disease (CKD), but the results of previous research were limited and inconsistent. The purpose of this study was to assess the relationships of PM2.5 and PM10 with CKD. This study was a cohort study based on the physical examination data of 2082 Beijing residents from 2013 to 2018 in the Beijing Health Management Cohort (BHMC). A land-use regression model was used to estimate the individual exposure concentration of air pollution based on the address provided by each participant. CKD events were identified based on self-report or medical evaluation (estimated glomerular filtration rate, eGFR less than 60 ml/min/1.73 m2). Finally, the associations of PM2.5 and PM10 with CKD were calculated using univariate and multivariate logistic regression models. During the research period, we collected potentially confounding information. After adjusting for confounders, each 10 μg/m3 increase in PM2.5 and PM10 exposure was associated with an 84% (OR: 1.84; 95% CI: 1.45, 2.33) and 37% (OR: 1.37; 95% CI: 1.15, 1.63) increased risk of CKD. Adjusting for the four common gaseous air pollutants (CO, NO2, SO2, O3), the effect of PM2.5 and PM10 on CKD was significantly enhanced, but the effect of PM10 was no longer significant in the multi-pollutant model. The results of the stratified analysis showed that PM2.5 and PM10 were more significant in males, middle-aged and elderly people over 45 years old, smokers, drinkers, BMI ≥ 24 kg/m2, and abnormal metabolic components. In conclusion, long-term exposure to ambient PM2.5 and PM10 was associated with an increased risk of CKD. [ABSTRACT FROM AUTHOR]

Abstract: It is noted that the data for ozone precursors and meteorological variables exhibit non-normal distributions. Thus, to detect surface ozone exceedance days, we propose a principal component analysis (PCA) model with a non-parametric T 2 control chart. The input variables include concentrations of ozone (O3), nitrogen oxide (NO), and nitrogen dioxide (NO2), wind speed (WS), relative humidity (RH), solar radiation (SR), surface and aloft temperatures (T). In addition, process variation indicators of meteorological factors are proposed to capture dynamic weather patterns. Ozone precursors and meteorological measurements of non-exceedance days during extended summers (May 16th–Oct. 15th) from 2000 to 2007, which include 1153days, are used to train a PCA model for the Livermore Valley, CA. Data of ozone exceedance days for the same period are used for validation of the model. Summer data from 2008 to 2009, which include 11 exceedance days, are used to test this prediction model. An ozone exceedance day is triggered when any T 2 value of this day exceeds the non-parametric T 2 control limit. Compared to a conventional PCA with a Hotelling T 2 control chart, the true prediction rate (TPR) of ozone exceedance days using this PCA model with a non-parametric T 2 control chart is increased from 45.45% to 72.73%. When process variation indicators (PVIs) are introduced into the PCA model with a non-parametric T 2 control chart, the TPR of ozone exceedance days is shown to increase to 100%. [Copyright &y& Elsevier]

Objective: To investigate the association between long-term fine particulate matter(PM_(2.5)) exposure and the risk of chronic kidney disease(CKD) in people with abnormal metabolism syndrome(MS) components., Methods: Based on health checkup data from a hospital in Beijing, a retrospective cohort study was used to collect annual checkup data from 2013-2019. A questionnaire was used to obtain information on demographic characteristics and lifestyle habits. We measured blood pressure, height, weight, waist circumference, concentrations of triglycerides(TG), fasting glucose, and high-density lipoprotein cholesterol(HDL-C). Longitude and latitude were also extracted from the addresses of the study subjects for pollutant exposure data estimation. Logistic regression models were used to explore the estimated effect of long-term PM_(2.5) exposure on the risk of CKD prevalence in people with abnormal MS components. Two-pollutant and multi-pollutant models were developed to test the stability of these result. Subgroup analysis was conducted based on age, the presence of MS, individual MS component abnormalities, and dual-component MS abnormalities., Results: The study included 1540 study subjects with abnormal MS components at baseline, 206 with CKD during the study period. The association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions was statistically significant, with a 2.26-fold increase in risk of CKD for every 10 μg/m~3 increase in PM_(2.5) exposure(OR=3.26, 95% CI 2.72-3.90). The result in the dual-pollutant models and multi-pollutant models suggested that the association between long-term PM_(2.5) exposure and increased risk of CKD in people with abnormal MS fractions remained stable after controlling for contemporaneous confounding by other air pollutants. The result of subgroup analysis revealed that individuals aged 45 or older, without MS, with TG<1.7 mmol/L, HDL-C≥1.04 mmol/L, without hypertension, and with central obesity and high blood sugar had a stronger association between PM_(2.5) exposure and CKD-related health effects., Conclusion: Long-term exposure to PM_(2.5) may increase the risk of CKD in people with abnormal MS components. More attention should be paid to middle-aged and elderly people aged ≥45 years, people with central obesity and hyperglycemia.