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• Short-term daily total PM 2.5 exposure is associated with work loss due to sickness. • Association of PM 2.5 with work loss becomes stronger when exposed to higher wildfire smoke. • Federal and state PM 2.5 standards could be further strict to protect public health. Few studies investigated the impact of particulate matter (PM 2.5) on some symptom exacerbations that are not perceived as severe enough to search for medical assistance. We aimed to study the association of short-term daily total PM 2.5 exposure with work loss due to sickness among adults living in California. We included 44,544 adult respondents in the workforce from 2015 to 2018 California Health Interview Survey data. Daily total PM 2.5 concentrations were linked to respondents' home addresses from continuous spatial surfaces of PM 2.5 generated by a geostatistical surfacing algorithm. We estimated the effect of a 2-week average of daily total PM 2.5 exposure on work loss using logistic regression models. About 1.69% (weighted percentage) of adult respondents reported work loss in the week before the survey interview. The odds ratio of work loss was 1.45 (odds ratio [OR] = 1.45, 95% confidence interval [CI]: 1.03, 2.03) when a 2-week average of daily total PM 2.5 exposure was higher than 12 µg/m3. The OR for work loss was 1.05 (95% CI: 0.98, 1.13) for each 2.56ug/m3 increase in the 2-week average of daily total PM 2.5 exposure, and became stronger among those who were highly exposed to wildfire smoke (OR = 1.06, 95% CI: 1.00, 1.13), compared to those with lower wildfire smoke exposure (OR = 1.04, 95% CI: 0.79, 1.39). Our findings suggest that short-term ambient PM 2.5 exposure is positively associated with work loss due to sickness and the association was stronger among those with higher wildfire smoke exposure. It also indicated that the current federal and state PM 2.5 standards (annual average of 12 µg/m3) could be further strengthened to protect the health of the citizens of California. [ABSTRACT FROM AUTHOR]

Regulatory monitoring networks are often too sparse to support community-scale PM 2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM 2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM 2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM 2.5 concentrations at a 1-km spatial resolution using three different PM 2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM 2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM 2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact. • Ground-level PM 2.5 was assessed with low-cost, regulatory, and satellite data. • Low-cost sensor measurements contributed to improved modeling performance. • Reasonable PM 2.5 spatial details were revealed due to abundant low-cost data. • Remaining uncertainty in calibrated low-cost data still affected modeling precision. [ABSTRACT FROM AUTHOR]