Studies worldwide have estimated the number of deaths attributable to long-term exposure to fine airborne particles (PM_2.5), but limited information is available on short-term exposure, particularly in China. In addition, most existing studies have assumed that short-term PM_2.5-mortality associations were linear. For this reason, the use of linear exposure-response functions for calculating disease burden of short-term exposure to PM_2.5 in China may not be appropriate. There is an urgent need for a comprehensive, evidence-based assessment of the disease burden related to short-term PM_2.5 exposure in China. Here, we explored the non-linear association between short-term PM_2.5 exposure and all-cause mortality in 104 counties in China; estimated county-specific mortality burdens attributable to short-term PM_2.5 exposure for all counties in the country and analyzed spatial characteristics of the mortality burden due to short-term PM_2.5 exposure in China. The pooled PM_2.5-mortality association was non-linear, with a reversed J-shape. We found an approximately linear increased risk of mortality from 0 to 62 μg/m³ and decreased risk from 62 to 250 μg/m³. We estimated a total of 169,862 additional deaths from short-term PM_2.5 exposure throughout China in 2015. Models using linear exposure-response functions for the PM_2.5-mortality association estimated 32,186 deaths attributable to PM_2.5 exposure, which is 5.3 times lower than estimates from the non-linear effect model. Short-term PM_2.5 exposure contributed greatly to the death burden in China, approximately one seventh of the estimates from the chronic effect. It is essential and crucial to incorporate short-term PM_2.5-related mortality estimations when considering the disease burden attributable to PM_2.5 in developing countries such as China. Traditional linear effect models likely underestimated the mortality burden due to short-term exposure to PM_2.5.

Background: Particulate matter pollution has become a growing health concern over the past few decades globally. The problem is especially evident in China, where particulate matter levels prior to 2013 are publically unavailable. We conducted a systematic review of scientific literature that reported fine particulate matter (PM_2.5) concentrations in different regions of China from 2005 to 2016. Methods: We searched for English articles in PubMed and Embase and for Chinese articles in the China National Knowledge Infrastructure (CNKI). We evaluated the studies overall and categorized the collected data into six geographical regions and three economic regions. Results: The mean (SD) PM_2.5 concentration, weighted by the number of sampling days, was 60.64 (33.27) μg/m³ for all geographic regions and 71.99 (30.20) μg/m³ for all economic regions. A one-way ANOVA shows statistically significant differences in PM_2.5 concentrations between the various geographic regions (F = 14.91, p < 0.0001) and the three economic regions (F = 4.55, p = 0.01). Conclusions: This review identifies quantifiable differences in fine particulate matter concentrations across regions of China. The highest levels of fine particulate matter were found in the northern and northwestern regions and especially Beijing. The high percentage of data points exceeding current federal regulation standards suggests that fine particulate matter pollution remains a huge problem for China. As pre-2013 emissions data remain largely unavailable, we hope that the data aggregated from this systematic review can be incorporated into current and future models for more accurate historical PM_2.5 estimates.