Household PM2 Research Articles

Algorithm-driven estimation of household cooking activity and its impact on indoor PM2.5 assessments

BackgroundHousehold PM2.5 exposures have adverse health effects, and cooking behaviors are an important source of PM2.5 in the home. There is a need for accurate measures of cooking activity to better understand its associations with household PM2.5 since self-reported surveys are often subject to recall bias and misreporting of cooking events. ObjectiveIn this study, we aimed to address limitations associated with a self-reported cooking metric, by using temperature data to estimate cooking activity. MethodsWe developed an algorithm to identify cooking events at the 5-minute level using real-time temperature data measured near the stove and in the living room, across 148 households in Chelsea and Dorchester, MA. We compared the number of cooking events identified by this algorithm with cooking events self-reported by participants in daily activity logs and survey responses, and further assessed how these metrics differed with respect to their associations with occurrence of peak PM2.5, in mixed effects logistic regression models. ResultsWe found that 65 % of the cooking events identified by the algorithm were not reported by participants. Furthermore, households classified as frequent vs infrequent cooking households using the algorithm had a larger difference in indoor PM2.5 levels, compared to households classified by self-report. In mixed effects logistic regression models for elevated household PM2.5 levels, we observed much stronger associations between household PM2.5 and algorithm-derived cooking activity (OR: 2.85 [95 % CI: 2.76, 2.95]) as compared to the association between household PM2.5 and self-reported cooking activity (OR: 1.22 [95 % CI: 1.17, 1.27] for stove use and OR: 1.67 [95 % CI: 1.58, 1.76] for grill use/frying/broiling/sauteing). SignificanceOverall, the algorithm developed in this study presents a data-driven approach to collecting cooking activity data in U.S. households, that may be more indicative of actual cooking events and also more predictive of household PM2.5 in indoor environmental models.

Is air pollution exposure linked to household income? Spatial analysis of Community Multiscale Air Quality Model results for Madrid

This study explores the potential correlation between income and exposure to air pollution for the city of Madrid, Spain and its neighboring municipalities. Madrid is a well-known European air pollution hotspot with a high mortality burden attributable to nitrogen dioxide (NO2) and fine particulate matter (PM2.5). Statistical analyses were carried out using electoral district level data on gross household income (GHI), and NO2 and PM2.5 concentrations in air obtained from a mesoscale air quality model for the study area. We applied linear regression, bivariate spatial correlation analysis, spatial autoregression and geographically weighted regression to explore the relationship between contaminants and income. Three different strategies were adopted to harmonize data for analysis. While some strategies suggested a link between income and air pollution, others did not, highlighting the need for multiple different approaches where uncertainty is high. Our findings offer important lessons for future spatial geographical studies of air pollution in cities worldwide. In particular we highlight the limitations of census-scale socio-economic data and the lack of non-model derived high-resolution air quality measurement data for many cities and offers lessons for policy makers on improving the integration of these types of essential public information.

Global Burden of Lung Cancer Attributable to Household Fine Particulate Matter Pollution in 204 Countries and Territories, 1990 to 2019

IntroductionHousehold particulate matter (PM) air pollution is substantially associated with lung cancer. Nevertheless, the global burden of lung cancer attributable to household PM2.5 is still uncertain. MethodsIn this study, data from the Global Burden and Disease Study 2019 are used to thoroughly assess the burden of lung cancer associated with household PM2.5. ResultsThe number of deaths and disability-adjusted life-years (DALYs) attributable to household PM2.5 was found to be 0.08 million and 1.94 million, respectively in 2019. Nevertheless, the burden of lung cancer attributable to household PM2.5 decreased from 1990 to 2019. At the sociodemographic index (SDI) district level, the middle SDI region had the most number of lung cancer deaths and DALYs attributable to household PM2.5. Moreover, the burden of lung cancer was mainly distributed in low-SDI regions, such as Sub-Saharan Africa. Conversely, in high-SDI regions, the age-standardized mortality rate and age-standardized DALY rate of lung cancer attributable to household PM2.5 exhibit the most rapid declines. The burden of lung cancer attributable to household PM2.5 is heavier for men than for women. The sex difference is more obvious in the elderly. ConclusionsThe prevalence of lung cancer attributable to household PM2.5 has exhibited a declining trend from 1990 to 2019 owing to a concurrent decline in household PM2.5 exposure.

Indoor air quality in Kazakh households: Evaluating PM2.5 levels generated by cooking activities

AbstractThe present study introduces a concentration estimation model for indoor inhalable fine particles (PM2.5) during cooking activities in typical Kazakh houses, which are generally poorly ventilated with high emission levels. The aim of the present work is to identify factors influencing PM2.5 concentrations during cooking and elucidate the mechanisms underlying the build‐up and reduction of PM2.5 concentrations. These are achieved through a methodology that combines PM2.5 sampling, monitoring, and modeling to predict household PM2.5 levels and estimate daily concentrations. Specifically, USEPA's IAQX v1.1 was employed to simulate the one‐zone concept (kitchen) for concentrations related to cooking activities in several households. The results reveal that PM2.5 concentrations varied between 13 and 266 μg/m3 during cooking activities. Factors such as kitchen size, air exchange characteristics, and the type of food and cooking style were identified as important, influencing the observed concentrations. The model accurately captured concentration trends (R > 0.9). However, certain predictions tended to overestimate the measurements, attributing to inaccuracies in selecting air exchange and emission rates. Cooking activities contributed to household air pollutant (HAP) PM2.5 levels ranging from 9% to 94%. Notably, during the non‐heating period of the year (corresponding to the warmer half of the year), the impact of cooking became more significant and was identified as a major contributor to indoor PM2.5 concentrations. Conversely, during the heating period (i.e., the colder part of the year), outdoor PM levels and household ventilation practices played primary roles in regulating indoor air concentrations. This present study presents one of the initial efforts to assess household air pollutants in Central Asia, providing foundation and insights into the indoor air quality of Kazakh houses, where the understanding of indoor air quality remains limited. Future research recommendations include developing advanced models that account for individual activity patterns and specific house types for improved accuracy and representativeness.

Effects of long-term indoor air purification intervention on cardiovascular health in elderly: a parallel, double-blinded randomized controlled trial in Hong Kong

Ambient fine particulate matter (PM2.5) is a leading environmental risk factor globally, and over half of the associated disease burden are caused by cardiovascular disease. Numerous randomized controlled trials (RCT) have investigated the short-term cardiovascular benefits of indoor air purifiers (IAPs), but major knowledge gaps remain on their longer-term benefits. In this 1-year, randomized, double-blinded, parallel controlled trial of 47 elderly (ntrue-purification = 24; nsham-purification = 23) aged ≥70 years, true-purification reduced household PM2.5 levels by 28% and maintained lower exposure throughout the year compared to the sham-purification group. After 12 months of intervention, a significant reduction of diastolic blood pressure was found in the true-purification versus sham-purification group (-4.62 [95% CI: -7.28, -1.96] mmHg) compared to baseline measurement prior to the intervention, whereas systolic blood pressure showed directionally consistent but statistically non-significant effect (-2.49 [95% CI: -9.25, 4.28] mmHg). Qualitatively similar patterns of associations were observed for pulse pressure (-2.30 [95% CI: -6.57, 1.96] mmHg) and carotid intima-media thickness (-10.0% [95% CI: -24.8%, 4.7%]), but these were not statistically significant. Overall, we found suggestive evidence of cardiovascular benefits of long-term IAPs use, particularly on diastolic blood pressure. Evidence on other longer-term cardiovascular traits is less clear. Further trials with larger sample sizes and long-term follow-up are needed across diverse populations to evaluate the cardiovascular benefits of IAPs.

Comparison of Real‐Time PM2.5 Between Rural and Urban Homes in Southwest China Based on Field Measurement: Insight Into the Cooking Activity Contribution

People generally spend most of their time indoors; thus, household air pollution (HAP), especially indoor air pollution, is of paramount importance. In this study, real‐time particulate matter (PM2.5) monitors were used to collect high temporal resolution concentrations of fine PM2.5 in different microenvironments from 104 urban and rural households in Guizhou Province to investigate the urban–rural difference in HAP and the contribution of cooking activity to HAP. Results showed that PM2.5 concentrations among different rural microenvironments varied largely, ranging from 26.10 ± 33.27 (mean ± standard deviation) in the bedroom to 69.20 ± 72.50 μg/m3 in the kitchen while varying slightly across various urban microenvironments, ranging from 32.30 ± 17.5 in the living room to 34.65 ± 16.37 μg/m3 in the kitchen. Cooking activity can rapidly increase PM2.5 concentrations in the kitchen, whose peaks were at least five times higher than baseline levels. The contribution of cooking to the kitchen PM2.5 was 18.43 ± 12.17% and 5.39 ± 4.15% in rural and urban homes, respectively. Biomass burning affected indoor PM2.5 concentrations significantly, where rural households using biomass had the highest PM2.5 levels than households burning other fuels. This study identified the urban–rural disparities in household PM2.5 pollution and the contributions of cooking activities to HAP, illustrating the importance of cooking activities to indoor air pollution, which can assist in controlling indoor air pollution.

Household air pollution exposure and risk of tuberculosis: a case–control study of women in Lilongwe, Malawi

IntroductionGlobally, 3–4 billion people rely on solid fuels for cooking, and 1 billion use kerosene to light their homes. While household air pollution (HAP) emitted from burning these fuels has...

Personal, household, and community PM2.5 exposure by cooking and heating fuel use and other key characteristics in the CKB-Air study

Personal, household, and community PM2.5 exposure by cooking and heating fuel use and other key characteristics in the CKB-Air study

Assessing household fine particulate matter (PM2.5) through measurement and modeling in the Bangladesh cook stove pregnancy cohort study (CSPCS)

Biomass fuel burning is a significant contributor of household fine particulate matter (PM2.5) in the low to middle income countries (LMIC) and assessing PM2.5 levels is essential to investigate exposure-related health effects such as pregnancy outcomes and acute lower respiratory infection in infants. However, measuring household PM2.5 requires significant investments of labor, resources, and time, which limits the ability to conduct health effects studies. It is therefore imperative to leverage lower-cost measurement techniques to develop exposure models coupled with survey information about housing characteristics. Between April 2017 and March 2018, we continuously sampled PM2.5 in three seasonal waves for approximately 48-h (range 46 to 52-h) in 74 rural and semi-urban households among the participants of the Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS). Measurements were taken simultaneously in the kitchen, bedroom, and open space within the household. Structured questionnaires captured household-level information related to the sources of air pollution. With data from two waves, we fit multivariate mixed effect models to estimate 24-h average, cooking time average, daytime and nighttime average PM2.5 in each of the household locations. Households using biomass cookstoves had significantly higher PM2.5 concentrations than those using electricity/liquefied petroleum gas (626 μg/m3 vs. 213 μg/m3). Exposure model performances showed 10-fold cross validated R2 ranging from 0.52 to 0.76 with excellent agreement in independent tests against measured PM2.5 from the third wave of monitoring and ambient PM2.5 from a separate satellite-based model (correlation coefficient, r = 0.82). Significant predictors of household PM2.5 included ambient PM2.5, season, and types of fuel used for cooking. This study demonstrates that we can predict household PM2.5 with moderate to high confidence using ambient PM2.5 and household characteristics. Our results present a framework for estimating household PM2.5 exposures in LMICs, which are often understudied and underrepresented due to resource limitations.

Characterising personal, household, and community PM2.5 exposure in one urban and two rural communities in China

BackgroundCooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours. MethodsWe undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed. FindingsOverall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31). ConclusionSolid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5.

Environmental factors and incidence of thyroid cancer in the world (1990-2019): an ecological study.

Thyroid cancer (TC) is the most common endocrine malignancy. Environmental risk factors such as presence of pollutants in air as well as the combustion of fossil fuels or carbon as a cooking habit in closed environments inside houses affect thyroid hormonal homeostasis and diseases. This study aimed to estimate the association between environmental risk factors and the incidence of thyroid cancer worldwide from 1990 to 2019 including particulate pollutants coming from fossil fuels employed in closed environments. Data on the incidence of thyroid cancer and some environmental risk factors were extracted from the Global Cancer Observatory (GLOBOCAN) for 204 countries and territories from 1990 to 2019. Pearson's correlation coefficient was used to determine the correlation between the thyroid cancer incidence and environmental risk factors. Finally, a generalized additive model was fitted for modeling. R 3.5.0 was used for analysis of the data. The most relevant results showed that the age-standardized incidence rate (ASIR) of thyroid cancer has a positive and significant correlation with environmental air pollution by O3 (r=0.63, P value<0.001), by particulate matter pollution (r=0.23, P value<0.001), and by household PM2.5 air pollution (r=0.52, P value≤0.001). In contrast, the correlation between ASIR and high temperature (T>25.6°C) (r=-0.27, P value<0.001) is negative and significant. The modeling results showed that particulate matter pollution and O3 pollution and household PM2.5 air pollution which originated from solid fuels are risk factors for thyroid cancer. Therefore, more research in this field is necessary in areas with high levels of air pollution at the national and international levels.

Effect of PM2.5 air pollution on the global burden of lower respiratory infections, 1990–2019: A systematic analysis from the Global Burden of Disease Study 2019

Particulate matter (PM) air pollution is closely related to lower respiratory infections (LRIs). However, the global LRI burden attributable to PM remains unclear. Here, we provide a comprehensive assessment of the PM2.5-attributable LRI burden using data from the Global Burden and Disease Study (GBD) 2019. We found that PM2.5 air pollution contributed to approximately 0.7 million deaths and 37.6 million disability-adjusted life years (DALYs) of LRIs in 2019. The LRI burden attributable to PM2.5 has decreased from 1990 to 2019, with a more pronounced decrease in household PM2.5 than in ambient PM2.5. Unlike the decreasing trend in LRI burden due to household PM2.5 worldwide, nearly one fifth of countries experienced an increase of LRI burden due to ambient PM2.5. The burden was unevenly distributed to less developed countries, mainly in Sub-Saharan Africa. All age groups experienced a decrease in the PM2.5-attributable burden, with the most significant decrease in children younger than 10 years. Notably, individuals aged 20–84 years experienced an increase in the LRI burden attributable to ambient PM2.5. Males had higher burden than females in the elder age and higher SDI regions. This study provided an evidence-based guidance for the prevention of LRIs and control of PM2.5 air pollution.

Characteristics and Differences of Household Fine Particulate Matter Pollution Caused by Fuel Burning in Urban and Rural Areas in China

Objective To explore the overall level,distribution characteristics,and differences in household fine particulate matter (PM2.5) pollution caused by fuel burning in urban and rural areas in China. Methods The relevant articles published from 1991 to 2021 were retrieved and included in this study.The data including the average concentration of household PM2.5 and urban and rural areas were extracted,and the stoves and fuel types were reclassified.The average concentration of PM2.5 in different areas was calculated and analyzed by nonparametric test. Results The average household PM2.5 concentration in China was (178.81±249.91) μg/m3.The mean household PM2.5 concentration was higher in rural areas than in urban areas[(206.08±279.40) μg/m3 vs. (110.63±131.16) μg/m3;Z=-5.45,P<0.001] and higher in northern areas than in southern areas[(224.27±301.66) μg/m3 vs.(130.11±140.61) μg/m3;Z=-2.38,P=0.017].The north-south difference in household PM2.5 concentration was more significant in rural areas than in urban areas[(324.19±367.94) μg/m3 vs.(141.20±151.05) μg/m3,χ2=-5.06,P<0.001].The PM2.5 pollution level showed differences between urban and rural households using different fuel types (χ2=92.85,P<0.001),stove types (χ2=74.42,P<0.001),and whether they were heating (Z=-4.43,P<0.001).Specifically,rural households mainly used solid fuels (manure,charcoal,coal) and traditional or improved stoves,while urban households mainly used clean fuels (gas) and clean stoves.The PM2.5 concentrations in heated households were higher than those in non-heated households in both rural and urban areas (Z=-4.43,P<0.001). Conclusions The household PM2.5 pollution caused by fuel combustion in China remains a high level.The PM2.5 concentration shows a significant difference between urban and rural households,and the PM2.5 pollution is more serious in rural households.The difference in the household PM2.5 concentration between urban and rural areas is more significant in northern China.PM2.5 pollution in the households using solid fuel,traditional stoves,and heating is serious,and thus targeted measures should be taken to control PM2.5 pollution in these households.

Second-hand smoke and fine particulate matter exposures among multi-unit housing residents in Singapore: A pilot study

Globally, approximately 1.2 million deaths among non-smokers are attributed to second-hand smoke (SHS) per year. Multi-unit housing is becoming the common type of residential dwelling in developed cities and the issue of neighbour SHS is of rising concern especially as ‘Work From Home’ became the norm during and post COVID-19 pandemic. The objective of this pilot study is to measure and compare the air quality of households that are exposed to SHS and unexposed households among smoking and non-smoking households in Singapore. A total of 27 households were recruited from April to August 2021. Households were categorized into smoking households with neighbour SHS, smoking households without neighbour SHS, non-smoking households with neighbour SHS, and non-smoking household without neighbour SHS. Air quality of the households was measured using calibrated particulate matter (PM2.5) sensors for 7–16 days. Socio-demographic information and self-reported respiratory health were collected. Regression models were used to identify predictors associated with household PM2.5 concentrations and respiratory health. Mean PM2.5 concentration was significantly higher among non-smoking households with neighbour SHS (n = 5, mean = 22.2, IQR = 12.7) than in non-smoking household without neighbour SHS (n = 2, mean = 4.1, IQR = 5.8). Smoking activity at enclosed areas in homes had the lowest PM2.5 concentration (n = 7 mean = 15.9, IQR = 11.0) among the three smoking locations. Exposure to higher household PM2.5 concentration was found to be associated with poorer respiratory health. A ‘smoke-free residential building’ policy is recommended to tackle the issue of rising neighbour SHS complaints and health concerns in densely populated multi-unit housing in Singapore. Public education campaigns should encourage smokers to smoke away from the home to minimize SHS exposure in household members.

Cohort profile: Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS)

PurposeThe Cook Stove Pregnancy Cohort Study (CSPCS) was designed to assess the effects of biomass fuel use on household air pollution (HAP) as well as the effects of HAP (fine...

Impacts of household PM2.5 pollution on blood pressure of rural residents: Implication for clean energy transition

High blood pressure associated with PM2.5 exposure is of great concern, especially for rural residents exposed to high PM2.5 levels. However, the impact of short-term exposure to high PM2.5 on blood pressure (BP) has not been well elucidated. Thus, this study aims to focus on the association between short-term PM2.5 exposure with BP of rural residents and its variation between summer and winter. Our results showed that the summertime PM2.5 exposure concentration was 49.3 ± 20.6 μg/m3, among which, mosquito coil users had 1.5-folds higher PM2.5 exposure than non-mosquito coil users (63.6 ± 21.7 vs 43.0 ± 16.7 μg/m3, p < 0.05). The mean systolic and diastolic BP (SBP and DBP, respectively) of rural participants were 122 ± 18.2 and 76.2 ± 11.2 mmHg in summer, respectively. The PM2.5 exposure, SBP, and DBP in summer were 70.7 μg/m3, 9.0 mmHg, and 2.8 mmHg lower than that in winter, respectively. Furthermore, the correlation between PM2.5 exposure and SBP was stronger in winter than that in summer, possibly due to higher PM2.5 exposure levels in winter. The transition of household energy from solid fuels in winter to clean fuels in summer would be benefit to the decline of PM2.5 exposure as well as BP. Results from this study suggested that the reduction of PM2.5 exposure would have positive effect on human health.

Identification of Factors Determining Household PM2.5 Variations at Regional Scale and Their Implications for Pollution Mitigation.

Indoor PM2.5, particulate matter no more than 2.5 μm in aerodynamic equivalent diameter, has very high spatiotemporal variabilities; and exploring the key factors influencing the variabilities is critical for purifying air and protecting human health. Here, we conducted a longer-term field monitoring campaign using low-cost sensors and evaluated inter- and intra-household PM2.5 variations in rural areas where energy or stove stacking is common. Household PM2.5 varied largely across different homes but also within households. Using generalized linear models and dominance analysis, we estimated that outdoor PM2.5 explained 19% of the intrahousehold variation in indoor daily PM2.5, whereas factors like the outdoor temperature and indoor-outdoor temperature difference that was associated with energy use directly or indirectly, explained 26% of the temporal variation. Inter-household variation was lower than intrahousehold variation. The inter-household variation was strongly associated with distinct internal sources, with energy-use-associated factors explaining 35% of the variation. The statistical source apportionment model estimated that solid fuel burning for heating contributed an average of 31%-55% of PM2.5 annually, whereas the contribution of sources originating from the outdoors was ≤10%. By replacing raw biomass or coal with biomass pellets in gasifier burners for heating, indoor PM2.5 could be significantly reduced and indoor temperature substantially increased, providing thermal comforts in addition to improved air quality.

Global, regional, and national burden of preterm birth attributable to ambient and household PM2.5 from 1990 to 2019: Worsening or improving?

BackgroundMaternal exposure to fine particular matter (PM2.5) during pregnancy, including ambient and household PM2.5, has been linked with increased risk of preterm birth (PTB). However, the global spatio-temporal distribution of PTB-related deaths and disability-adjusted life years (DALYs) attributable to PM2.5 is not well documented. We estimated the global, regional, and national patterns and trends of PTB burden attributable to both ambient and household PM2.5 from 1990 to 2019. MethodsBased on the Global Burden of Disease Study (GBD) 2019 database, we obtained the numbers of deaths and DALYs as well as age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) of PTB attributable to total, ambient, and household PM2.5 by socio-demographic index (SDI) and sex during 1990–2019. The average annual percentage changes (AAPCs) were calculated to assess the temporal trends of attributable burdens. ResultsIn 2019, 126,752 deaths and 11.3 million DALYs related to PTB worldwide (two-thirds in Western Sub-Saharan Africa and South Asia) could be caused by excess PM2.5 above the theoretical minimum-risk exposure level (TMREL), of which 39 % and 61 % were attributable to ambient PM2.5 and household PM2.5, respectively. From 1990 to 2019, the global ASMR due to ambient PM2.5 increased slightly by 7.08 % whereas that due to household PM2.5 decreased substantially by 58.81 %, although the latter still dominated the attributable PTB burden, especially in low and low-middle SDI regions. Similar results were also observed for ASDRs. In addition, PTB burden due to PM2.5 was higher in male infants and in lower SDI regions. ConclusionsGlobally in 2019, PM2.5 remains a great concern on the PTB burden, especially in Western Sub-Saharan Africa and South Asia. Between 1990 and 2019, age-standardized burden of PTB due to ambient PM2.5 increased globally, while that due to household PM2.5 decreased markedly but still dominated in low and low-middle SDI regions.

Global, regional, and national burden of ambient and household PM2.5-related neonatal disorders, 1990–2019

Previous studies have shown a relationship between fine particulate matter (PM2.5) exposure and an increased risk of neonatal disorders. Considering the huge burden of neonatal disorders, we assessed spatiotemporal trends of neonatal disorders burden caused by ambient and household PM2.5 at the global, regional, and national levels from 1990 to 2019. The number, rate, and population attributable fraction (PAF) of ambient and household PM2.5-related neonatal disorders disability-adjusted life years (DALYs) in 204 countries and territories from 1990 to 2019 were obtained from the Global Burden of Disease Study 2019 to measure the related neonatal disorders burden by age, sex, subtype, and region. Estimated annual percentage change (EAPC) was estimated to quantify temporal trends. In 2019, approximately a fifth of the global neonatal disorders burden was attributable to PM2.5 exposure, with 7.54% for ambient PM2.5 and 13.23% for household PM2.5. Although the global neonatal disorders burden attributable to household PM2.5 has decreased substantially in the past 30 years, that attributable to ambient PM2.5 has increased, especially in lower sociodemographic index (SDI) regions. The highest rate and PAF of ambient PM2.5-related neonatal disorders DALYs in 2019 were in South Asia and East Asia, respectively, and the fastest increases were in Eastern Sub-Saharan Africa (for rate: EAPC = 2.55, 95% CI: 2.37–2.73) and South Asia (for PAF: EAPC = 3.88, 95% CI: 3.68–4.08). In addition, we found an inverted V-shaped between rates and PAFs of ambient PM2.5-related neonatal disorders DALYs in 2019, as well as corresponding EAPCs, and SDI, while rates and PAFs of household PM2.5-related neonatal disorders DALYs in 2019 were highly negatively correlated with SDI. In the past three decades, the global ambient PM2.5-related neonatal disorders burden largely increased, especially in lower SDI regions. Comparatively, the household PM2.5-related neonatal disorders burden decreased but still accounted for about two-thirds of the PM2.5-related neonatal disease burden.

Time Trends in Ischemic Heart Disease Mortality Attributable to PM2.5 Exposure in Southeastern China from 1990 to 2019: An Age-Period-Cohort Analysis.

Data were extracted from the Global Burden of Disease study 2019 (GBD2019). The magnitude and direction of the trends in IHD mortality attributable to PM2.5 exposure were analyzed by Joinpoint regression. The age-period-cohort (APC) model was used to evaluate the cohort and period effect. Age-standardized mortality rate (ASMR) of IHD attributable to PM2.5 exposure decreased from 1990 to 2019, with an average annual percentage change (AAPC) of -1.71% (95%CI: -2.02~-1.40), which, due to ambient PM2.5 (APM) exposure and household PM2.5 (HPM) exposure increased with AAPCs of 1.45% (95%CI: 1.18~1.72) and -8.27% (95%CI: -8.84~-7.69), respectively. APC analysis revealed an exponential distribution in age effects on IHD mortality attributable to APM exposure, which rapidly increased in the elderly. The risk for IHD mortality due to HPM exposure showed a decline in the period and cohort effects, which, due to APM, increased in the period and cohort effects. However, favorable period effects were found in the recent decade. The overall net drift values for APM were above zero, and were below zero for HPM. The values for local drift with age both for APM and HPM exposures were initially reduced and then enhanced. The main environmental risk factor for IHD mortality changed from HPM to APM exposure in Jiangsu Province, China. Corresponding health strategies and prevention management should be adopted to reduce ambient air pollution and decrease the effects of APM exposure on IHD mortality.