Deaths Attributable to Indoor PM 2.5 in Urban China When Outdoor Air Meets 2021 WHO Air Quality Guidelines

Exposure to ambient and indoor particle matter (PM2.5) leads to millions of premature deaths in China. In recent years, indoor air pollution and premature deaths associated with polluting fuel cooking demonstrate an abrupt decline. However, the driving forces behind the mortality change are still unclear due to the uncertainty in household fuel use prediction. Here, we propose an integrated approach to estimate the fuel use fractions and PM2.5-related deaths from outdoor and indoor sources during 2000–2020 across China. Our model estimated 1.67 and 1.21 million premature deaths attributable to PM2.5 exposure in 2000 and 2020, respectively. We find that the residential energy transition is associated with a substantial reduction in premature deaths from indoor sources, with 100,000 (95% CI: 76,000–122,000) for urban and 265,000 (228,000–300,000) for rural populations during 2000–2020. Economic growth is the dominant driver of fuel use transition and avoids 21% related deaths (357,000, 315,000–402,000) from polluting fuel cooking since 2000, which offsets the adverse impact of ambient emissions contributed by economic growth. Our findings give an insight into the coupled impact of socioeconomic factors in reshaping health burden in exposure pathways.

Section: Discussionmentioning

confidence: 99%

Economic Growth Facilitates Household Fuel Use Transition to Reduce PM_2.5-Related Deaths in China

Shao,

Liu,

Yang

et al. 2023

“…11−13 Studies on Chinese residences using clean energy or technology suggest that indoor sources contributed 40% (95% confidence interval: 0−86%) to PM 2.5 exposure in non-smoking households and 52% (95% confidence interval: 7−89%) in smoking households in urban China; 14 additionally, indoor sources contributed to 394 (95% confidence interval: 323−457) thousand deaths in urban China in 2019, accounting for 56% of the total deaths attributed to PM 2.5 . 15 To reduce health risks from indoor PM 2.5 exposure, the first indoor PM 2.5 standard in China was issued in 2022 with a value of 50 μg/m 3 at a 24 h average. 16 However, this standard is merely equivalent to the WHO Interim target 2 and is far higher than the WHO AQG level, below which there is no evidence of adverse health effects.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Indoor PM 2.5 can be classified into two sources: one originates from outdoors via ventilation or infiltration, and the other is emitted from indoor sources. A large amount of PM 2.5 is generated by indoor activities such as cooking and smoking. − Studies on Chinese residences using clean energy or technology suggest that indoor sources contributed 40% (95% confidence interval: 0–86%) to PM 2.5 exposure in non-smoking households and 52% (95% confidence interval: 7–89%) in smoking households in urban China; additionally, indoor sources contributed to 394 (95% confidence interval: 323–457) thousand deaths in urban China in 2019, accounting for 56% of the total deaths attributed to PM 2.5 . To reduce health risks from indoor PM 2.5 exposure, the first indoor PM 2.5 standard in China was issued in 2022 with a value of 50 μg/m 3 at a 24 h average .…”

Section: Introductionmentioning

confidence: 99%

Air Purifier Intervention to Remove Indoor PM_2.5 in Urban China: A Cost-Effectiveness and Health Inequality Impact Study

Zhang

Liu

et al. 2023

Self Cite

Using air purifiers is an intervention to reduce exposure to fine particulate matter (PM2.5) for health benefits. We performed a comprehensive simulation in urban China to estimate the cost-effectiveness of long-term use of air purifiers to remove indoor PM2.5 from indoor and ambient air pollution in five intervention scenarios (S1–S5), where the indoor PM2.5 targets were 35, 25, 15, 10, and 5 μg/m3, respectively. In scenarios S1 to S5, 5221 (95% uncertainty interval: 3886–6091), 6178 (4554–7242), 8599 (6255–10,109), 11,006 (7962–13,013), and 14,990 (10,888–17,610) thousand disability-adjusted-life-years (DALYs) can be avoided at the cost of 201 (199–204), 240 (238–243), 364 (360–369), 522 (515–530), and 921 (905–939) billion Chinese Yuan (CNY), respectively. A high disparity in per capita health benefits and costs was observed by city, which expanded with the decrease of the indoor PM2.5 target. The net benefits of using purifiers in cities varied across scenarios. Cities with a lower ratio of annual average outdoor PM2.5 concentration to gross domestic product (GDP) per capita tended to achieve higher net benefits in the scenario with a lower indoor PM2.5 target. Controlling ambient PM2.5 pollution and developing the economy can reduce the inequality in air purifier use across China.

“…In particular, infrared CO 2 sensors and optical particular counters (OPC) are ubiquitously deployed in various indoor environments and heating, ventilation, and air-conditioning (HVAC) systems. Indoor CO 2 concentration is often considered a proxy for ventilation and IAQ and has been linked with inhalation exposure, , cognitive tasks, , and transmission of infectious disease, , while particulate matter (PM) has known health consequences. , However, although most low-cost IAQ sensors show acceptable relative precision, they often suffer from poor absolute accuracy and drift issues . These drawbacks severely limit the application of low-cost sensors in IAQ assessment, exposure monitoring, and building certification.…”

Section: Introductionmentioning

confidence: 99%

Estimating Indoor Pollutant Loss Using Mass Balances and Unsupervised Clustering to Recognize Decays

Siegel

2023

Low-cost air quality monitors are increasingly being deployed in various indoor environments. However, data of high temporal resolution from those sensors are often summarized into a single mean value, with information about pollutant dynamics discarded. Further, low-cost sensors often suffer from limitations such as a lack of absolute accuracy and drift over time. There is a growing interest in utilizing data science and machine learning techniques to overcome those limitations and take full advantage of low-cost sensors. In this study, we developed an unsupervised machine learning model for automatically recognizing decay periods from concentration time series data and estimating pollutant loss rates. The model uses k-means and DBSCAN clustering to extract decays and then mass balance equations to estimate loss rates. Applications on data collected from various environments suggest that the CO2 loss rate was consistently lower than the PM2.5 loss rate in the same environment, while both varied spatially and temporally. Further, detailed protocols were established to select optimal model hyperparameters and filter out results with high uncertainty. Overall, this model provides a novel solution to monitoring pollutant removal rates with potentially wide applications such as evaluating filtration and ventilation and characterizing indoor emission sources.