Difference In PM2 Research Articles

Patterns and disparities in indoor particulate matter levels in selected primary schools in Kigali, Rwanda

Air pollution is a global environmental and public health challenge. There is limited evidence about the air quality in Rwanda, and the concentrations of particulate matter (PM), namely PM2.5 and PM10 in schools have not been well documented. This study evaluated patterns and disparities in indoor PM levels in selected primary schools in Kigali, Rwanda.The study collected PM2.5 and PM10 concentrations from six classrooms in six selected primary schools during the regular school study period in the dry season. Data were collected using mobile air sensors (purple air/PA–II–SD air quality) and an observation checklist. A Kruskal-Wallis test was performed to assess the difference in PM2.5 and PM10 concentrations between the six schools. The post-hoc Mann-Whitney test was used to compare all group pairs.The results indicated a significant difference in both the indoor PM2.5 concentration (H (5) = 41.01, p < 0.001) and the indoor PM10 concentration (H (5) = 38.5, p < 0.001). The maximum concentration observed was 133.6 μg/m3 for PM2.5 and 158.5 μg/m3 for PM10. Schools in highly exposed areas tended to have higher concentrations of PM than schools in moderately exposed areas. Specifically, the daily average concentration of PM2.5 in schools located in highly exposed areas ranged from 39 μg/m³ to 118 μg/m³, while PM10 levels ranged from 44.0 μg/m³ to 126 μg/m³. In contrast, schools in moderately exposed areas had daily PM2.5 average concentrations ranging from 32.0 μg/m³ to 111.0 μg/m³ and daily PM10 average concentrations ranging from 38.0 μg/m³ to 119 μg/m³.Overall, the recorded values for both PM2.5 and PM10 in all sampled schools were higher than the World Health Organization air quality guidelines. Indoor air quality is poorer in schools situated in highly exposed areas. This study suggests interventions to improve school air quality for the benefit of school communities.

The Role of Environmental Exposures on Survival After Non-Small Cell Lung Cancer Resection

BackgroundSocioeconomic status and pollution exposure have been described as risk factors for poor survival in patients with non-small cell lung cancer (NSCLC). However, the relationship between these factors is complex and inadequately studied. This study aimed to evaluate the relationship between environmental and social factors and their impact on survival after NSCLC resection. MethodsA prospective database for all patients with NSCLC who underwent primary resection from 2006 to 2021 was analyzed. Ambient fine particulate matter (air pollution smaller than 2.5 μm [PM2.5]), greenness, and deprivation index (a measure of neighborhood-level material deprivation composed of 6 factors) were linked to individual patients by geocoding their residential address. ResultsA total of 661 patients who underwent pulmonary resection for NSCLC were evaluated. Black patients had increased levels of community deprivation compared with White patients; however, there was no difference in PM2.5 exposure or overall survival between races. Increased PM2.5 exposure was an independent predictor of worse survival on univariable and multivariable analysis (hazard ratio, 1.06; P = .003). ConclusionsIncreased PM2.5 exposure is associated with worse overall survival in resected NSCLC and was a more significant factor than race and material deprivation in this population. Interventions to reduce environmental air pollution could improve lung cancer survival.

Air pollution, social engagement, and depression in older adults: Results from a Swedish population-based cohort study

Although emerging research has investigated the relationship between outdoor air pollution and depression risk in older adults, the results remain inconclusive. We aimed to determine the relationship between long-term exposure to ambient air pollution and depression among older adults and explore whether active social engagement may modify this association. At baseline (2001–2004), 2812 depression-free older adults from Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) were included. SNAC-K is a longitudinal population-based cohort in Stockholm, Sweden. Incident depression cases occurred during 2004–2013 were ascertained using the Diagnostic and Statistical Manual of Mental Disorders 4th Edition. Air pollution [particulate matter (PM) and nitrogen oxides (NOx)] at the residency were estimated using dispersion models. Social engagement was measured as active participation in social activities (at least twice/week) or inactive (less than twice/week) in the last 12 months. The hazard ratios (HR) and 95% confidence intervals of depression from air pollution exposure of 3-year moving average before diagnosis (1-μg/m3 difference in PM2.5 and PM10, and 10-μg/m3 difference in NOx) were obtained from Cox models considering greenspace and noise. A product term of air pollutant and social activity was added to test the multiplicative interaction and attributable proportion due to interaction was calculated for assessing additive interaction. We identified 137 (4.9%) incident depression cases. Participants exposed to higher concentrations of PM2.5, NOx, and PM10 had 53% (HR:1.53 [1.22, 1.93]), 26% (HR:1.26 [1.01, 1.58]), and 7% (HR:1.07 [0.98, 1.18]) increased hazard of depression, respectively. These associations were largely attenuated in people with active social engagement (HR for PM2.5: 1.04 [0.70, 1.55]; HR for PM10: 0.98 [0.81, 1.18]; and HR for NOx: 1.09 [0.71, 1.66]). Our findings suggest long-term exposure to air pollution may be a risk factor for depression among older adults. An active social engagement might however decrease this risk.

Long-term exposure to ambient fine particulate matter-bound polycyclic aromatic hydrocarbons and cancer mortality: A difference-in-differences approach

The association of ambient fine particulate matter (PM2.5) exposure with cancer mortality was controversial, which may ascribe to the difference in PM2.5 constituents. Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic constituents in PM2.5, which are suspected to account for PM2.5-induced cancer mortality but are yet to be investigated. We aimed to assess the association between long-term exposure to PM2.5-bound PAHs and cancer mortality and estimate the attributable mortality. A difference-in-differences approach was used to investigate the causal effect of long-term exposure to PM2.5-bound PAHs on cancer mortality. We divided Jiangsu province, China into 53 spatial units and summarized the annual number of cancer deaths in each spatial unit during 2016–2020. Annual population-weighted exposure to PM2.5-bound PAHs of each spatial unit was assessed by an inverse distance weighting method. The association between PM2.5-bound PAHs exposures and cancer mortality was evaluated by controlling spatial differences, temporal trends, PM2.5 mass exposures, temperatures, and socioeconomic status. Records of 793,269 cancer deaths were identified among 84.7 million population. Each ln-unit increase of exposure to total benzo[a]pyrene equivalents (∑BaPeq), total carcinogenic PAHs (∑PAH7c), and total PAHs (∑PAHs) was significantly associated with a 3.21%, 3.48%, and 2.64% increased risk of cancer mortality, respectively; the risk increased monotonically at low-level exposures but attenuated or flattened afterward (all p for nonlinearity <0.05). Similar exposure-response associations were identified for specific PAHs except that the associations for both fluoranthene and benzo[a]anthracene were linear. We estimated that exposure to ∑BaPeq, ∑PAH7c, and ∑PAHs contributed to 5.73%, 8.73%, and 7.33% of cancer deaths, respectively. In conclusion, long-term exposure to PM2.5-bound PAHs was associated with an increased risk of cancer mortality and contributed to substantial cancer deaths. Our findings highlight the importance to prevent deaths from cancer by reducing PM2.5-bound PAHs exposures and the necessity to take into consideration specific constituents in particulate pollution management in future.

Concurrent measurements of nitrate at urban and suburban sites identify local nitrate formation as a driver for urban episodic PM2.5 pollution

Nitrate (NO3−) is often among the leading components of urban particulate matter (PM) during PM pollution episodes. However, the factors controlling its prevalence remain inadequately understood. In this work, we analyzed concurrent hourly monitoring data of NO3− in PM2.5 at a pair of urban and suburban locations (28 km apart) in Hong Kong for a period of two months. The concentration gradient in PM2.5 NO3− was 3.0 ± 2.9 (urban) vs. 1.3 ± 0.9 μg m−3 (suburban) while that for its precursors nitrogen oxides (NOx) was 38.1 vs 4.1 ppb. NO3− accounted for 45 % of the difference in PM2.5 between the sites. Both sites were characterized to have more available NH3 than HNO3. Urban nitrate episodes, defined as periods of urban-suburban NO3− difference exceeding 2 μg m−3, constituted 21 % of the total measurement hours, with an hourly NO3− average gradient of 4.2 and a peak value of 23.6 μg m−3. Our comparative analysis, together with 3-D air quality model simulations, indicates that the high NOx levels largely explain the excessive NO3− concentrations in our urban site, with the gas phase HNO3 formation reaction contributing significantly during the daytime and the N2O5 hydrolysis pathway playing a prominent role during nighttime. This study presents a first quantitative analysis that unambiguously shows local formation of NO3− in urban environments as a driver for urban episodic PM2.5 pollution, suggesting effective benefits of lowering urban NOx.

Environmental correlates of adolescent active travel to school in Asia: An ecological study.

This study aimed to examine the association between country-level environmental correlates and the prevalence of active school travel (AST) in Asia and country-level differences in AST by age and sex. This ecological study involved 31 Asian countries. Dependent variables were AST prevalence, AST prevalence difference by age, and by sex. Independent variables were country-level environmental correlates extracted using publicly available datasets, classified into physical and social environments. Association estimates of each dependent variable and each of the independent variables were calculated using univariate linear regression. All variables were standardized to have a mean of 0 and a standard deviation of 1. Results showed that 1 standard deviation (SD) difference in urban population percentage, night-time light, secondary-school enrolment, and prevalence of adult insufficient physical activity were negatively associated with AST prevalence (SD difference: -0.44 (-0.78 to -0.09), -0.40 (-0.76 to -0.04), -0.39 (-0.74 to -0.04), and -0.40 (-0.76 to -0.03), respectively). A 1 SD difference in car per people was associated with a -0.46 (-0.84 to -0.09) difference of AST prevalence by age. A 1 SD difference in PM2.5 concentration and of prevalence of adult insufficient physical activity were associated with a difference of 0.38 (0.01-0.74) and 0.42 (0.03-0.80) difference of AST prevalence by sex. This study shows that Asian countries with a greater number of people living in urban areas, lower levels of overall adult physical activity and higher levels of night-time light have a lower prevalence of adolescent AST. Country-level physical and social environmental correlates explained some of the regional variance in AST. Future policy actions and interventions for the region need to be contextually sensitive to the environmental correlates that vary between countries.

Difference in PM2.5 Pollution and Transport Characteristics Between Urban and Suburban Areas

Based on multi-source observation data, such as lidar ceilometer, aircraft AMDAR, and conventional sites, combined with numerical simulation (CAMx-PSAT), this study took the typical cities of the Beijing-Tianjin-Hebei region-Beijing (BJ) urban area and suburbs (Miyun) and Shijiazhuang (SJZ) urban area and suburbs (Pingshan) as the case study areas. The differences in boundary layer height between urban areas and suburbs (ΔPBLH), surface PM2.5 mass concentration (ΔSurf_PM2.5), vertical PM2.5 mass concentration (ΔVert_PM2.5), and transmission flux intensity and height distribution characteristics were analyzed. The results showed:due to factors such as anthropogenic heat sources, short-wave radiation, and thermal turbulence, the annual average planetary boundary layer height in urban areas was 8%-29% higher than that in the suburbs, and in different seasons, the monthly average planetary boundary layer height in urban areas was 2% (April in SJZ)-47% (July in BJ) higher than that in the suburbs. Due to the combined effects of anthropogenic emissions, inversions, and atmospheric turbulence, the annual averageρ(PM2.5) in urban areas between 0-1260 m was higher than that in suburbs by 0.1 (SJZ)-29.7 (BJ) μg·m-3 and decreased with the increase in height. The annual average total net flux intensity in urban areas was much greater than that in suburbs, with outflows in urban areas and inflows in suburbs; due to the urban low pressure and the suburban high pressure, suburban thermal circulation was formed. The annual average total net flux intensity in BJ (44.77 t·d-1) was greater than that in SJZ (34.44 t·d-1). Affected by wind speed and PM2.5 mass concentration, between 0-1260 m, the fluxes in urban areas and suburbs and surrounding areas showed an obvious trend of increasing net flux intensity with the increase in height above the ground. Furthermore, the transmission exchange between urban areas and suburbs and surrounding areas in January and April had the most obvious impact on the environment. The intensity of the maximum net flux in the lower urban areas and the suburbs in different seasons was significantly different, and the difference between the two was 2.23-4.48 times; however, the height characteristic difference in the intensity of the maximum net flux was small, mainly located at 611-1260 m.

Technical note: Use of PM2.5 to CO ratio as an indicator of wildfire smoke in urban areas

Abstract. Wildfires and their resulting smoke are an increasing problem in many regions of the world. However, identifying the contribution of smoke to pollutant loadings in urban regions can be challenging at low concentrations due to the presence of the usual array of anthropogenic pollutants. Here we propose a method using the difference in PM2.5 to CO emission ratios between smoke and typical urban pollution. For temperate wildfires, the mean emission ratio of PM2.5 to CO is in the range of 0.14–0.18 g PM2.5 g CO−1, whereas typical urban emissions have a PM2.5 to CO emissions ratio that is lower by a factor of 2–20. This gives rise to the possibility of using this ratio as an indicator of wildfire smoke. We use observations at a regulatory surface monitoring site in Sparks, NV, for the period of May–September 2018–2021. There were many smoke-influenced periods from numerous California wildfires that burned during this period. Using a PM2.5 / CO threshold of 30.0 µgm-3ppm-1, we can split the observations into smoke-influenced and no-smoke periods. We then develop a Monte Carlo simulation, tuned to local conditions, to derive a set of PM2.5 / CO values that can be used to identify smoke influence in urban areas. From the simulation, we find that a smoke enhancement ratio of 140 µgm-3ppm-1 best fits the observations, which is significantly lower than the ratio observed in fresh smoke plumes (e.g., 200–300 µgm-3ppm-1). The most likely explanation for this difference is loss of PM2.5 during dilution and transport to warmer surface layers. We find that the PM2.5 / CO ratio in urban areas is an excellent indicator of smoke and should prove to be useful to identify biomass burning influence on the policy-relevant concentrations of both PM2.5 and O3. Using the results of our Monte Carlo simulation, this ratio can also quantify the influence of smoke on urban PM2.5.

Differences in Fine Particle Exposure and Estimated Pulmonary Ventilation Rate with Respect to Work Tasks of Wildland Firefighters at Prescribed Burns: A Repeated Measures Study.

Wildland firefighters (WLFFs) are exposed to a mixture of chemicals found in wildland fire smoke and emissions from nonwildland-fuel smoke sources such as diesel. We investigated compositional differences in exposure to particulate matter and explored differences in ventilation rate and potential inhaled dose relative to the work tasks of WLFFs. Repeated measures on ten professional and two volunteer firefighters were collected on prescribed burn and nonburn days. Personal monitoring consisted of real-time and gravimetric fine particulate matter (PM2.5), carbon monoxide (CO), and accelerometer measurements to estimate ventilation rate and potential dose of PM2.5. The fine particulate matter was analyzed for levoglucosan (LG) and light absorbing carbon as a surrogate for black carbon (BC). Breathing zone personal exposure concentrations of PM2.5, LG, BC, and CO were higher on burn days (P &lt; 0.05). Differences in exposure concentrations were observed between burn day tasks (P &lt; 0.05) with firefighters managing fire boundaries (holders) being exposed to higher CO and LG concentrations and less BC concentrations than those conducting lighting (lighters). While no statistical difference in PM2.5 exposure measures was observed between the two tasks, holders in the study tended to be exposed to higher PM2.5 concentrations (~1.4×), while lighters tended to have more inhaled amounts of PM2.5 (~1.3×). Our findings demonstrate possible diversity in the sources of particulate matter exposure at the fireline and suggest the potential importance of using dose as a metric of inhalation exposure in occupational or other settings.

Invited Perspective: HEPA Filters-An Effective Way to Prevent Adverse Air Pollution Effects on Neurodevelopment?

Invited Perspective: HEPA Filters-An Effective Way to Prevent Adverse Air Pollution Effects on Neurodevelopment?

Particulate Matter Exposure and the Changes in Immune Biomarkers: Effects of Biyeom-Go on the Nasal Mucosa of Patients with Allergic Rhinitis and a Particulate Matter-Treated Mouse Model

This study was to investigate the effects of Biyeom-go (BYG, an herbal formula) on immune biomarkers present in the nasal mucosa of patients with allergic rhinitis under exposure to particulate matter 2.5 (PM2.5), and on changes in goblet cells and immune biomarkers in mice under exposure to Korea diesel particulate matter (KDP20). Thirty patients showing characteristic allergic rhinitis symptoms were enrolled in Jeonju-si, Korea, and treated with BYG thrice a day for four weeks. Changes in the expression of immune biomarkers (interleukin 4 (IL-4), IL-5, IL-8, IL-13, IL-33, and thymic stromal lymphopoietin (TSLP) mRNA), total nasal symptom scores (TNSS), mini-rhinitis-specific quality of life questionnaire (RQLQ) results, and visual analog scale scores were evaluated after 4 weeks of treatment. Additionally, the difference in PM2.5 concentrations in the air in Jeonju-si, Korea (November, 2019 ∼ March, 2020), was analyzed to determine the change in TNSS. KDP20 (100 μg/mL) was exposed to C57BL/6 mice for 10 days; 0.05% Nasonex (a positive control, mometasone furoate), or BYG was administrated for 5 days twice a day. The expression of inflammatory factors was detected via qRT-PCR using nasopharynx tissue samples of mice. BYG treatment was found to be associated with significant improvement in total nasal symptoms, especially itching and sneezing (p < 0.0001), and mini-RQLQ after 4 weeks. IL-8 (p < 0.01), IL-33 (p < 0.01), and TSLP (p < 0.001) expression levels decreased after BYG treatment. In mice, administration of BYG reduced the number of goblet cells increased through KDP20 treatment. KDP20-induced immune biomarkers (IL-33, TSLP, tumor necrosis factor alpha, and IL-8) were also significantly downregulated in the nasopharynx tissue after BYG treatment. Therefore, BYG may show therapeutic effects against allergic rhinitis in humans, and it was confirmed that the expression of PM-induced inflammatory factors in mice was decreased via BYG treatment.

What Causes Differences in PM2.5 Concentration in China? Structures are more Important

This paper studies the effects of factors such as economic growth, industrial structure and governance on the differences in PM2.5 concentration among groups by using Oaxaca-Blinder (OB) decomposition and Recentered Influence Function (RIF) regressions. Specifically, we first explore the differences in PM2.5 concentration among cities and find that structure effects attributable to differences in returns to these factors contribute more to total difference in PM2.5 concentration than composition effects caused by differences in the means of these factors. Moreover, the offsetting effects of positive composition and negative structure effects drag the time trend of PM2.5 concentration downward. Besides, the negative impact of GDP per capita on PM2.5 concentration implies the existence of an inverted U-shaped environmental Kuznets curve (EKC) between PM2.5 concentration and GDP per capita. In sum, we find that the differences in returns to factors or structures of cities are the main causes of differences in PM2.5 concentration among cities in China compared to the differences in the means of factors.

Effect of omega-3 fatty acids on TH1/TH2 polarization in individuals with high exposure to particulate matter \u2264 2.5\u2009\u03bcm (PM2.5): a randomized, double-blind, placebo-controlled clinical study

BackgroundLong-term exposure to high concentrations of PM2.5 may cause immune system dysfunction and damage to the respiratory and cardiovascular systems. PM2.5 may cause CD4 + T helper cells to polarize toward TH1 or TH2 cell types, which may be associated with the onset and progression of many human diseases. Recent studies have shown that omega-3 fatty acids can regulate human immune function and reduce physiological damage caused by air pollution; however, only limited research has examined the therapeutic effects of omega-3 fatty acids on subjects with high exposure to PM2.5 in mass transit systems such as subways.MethodsThis study was designed as a prospective, randomized, double-blinded (to participants and researchers), placebo-controlled clinical trial. The research plan is to randomly select 120 eligible adults based on the difference in PM2.5 exposure in the Chengdu subway station. They should be aged 20–65 years old and work in the subway station more than or equal to 3 times a week, each time greater than or equal to 8 h, and had worked continuously in the subway station for more than 2 years. All participants will receive omega-3 fatty acids or placebo for 8 weeks. The primary outcomes will be changes in the TH1/TH2 cell polarization index and changes in serum cytokine concentrations. Secondary outcomes will be changes in early indicators of atherosclerosis, pulmonary function, COOP/WONCA charts, and scores on the Short-Form 36 Health Survey for quality of life. Results will be analyzed to evaluate differences in clinical efficacy between the two groups. A 6-month follow-up period will be used to assess the long-term value of omega-3 fatty acids for respiratory and cardiovascular disease endpoints.DiscussionWe will explore the characteristics of the TH1/TH2 cell polarization index in a population with high exposure to PM2.5. Omega-3 fatty acids and placebo will be compared in many ways to test the effect on people exposed to PM2.5 subway stations. This study is expected to provide reliable evidence to support the promotion of omega-3 fatty acids in clinical practice to protect individuals who are highly exposed to PM2.5.Trial registrationChinese Clinical Trial Registry ChiCTR2000038065. Registered on September 9, 2020

Role of Upwind Precipitation in Transboundary Pollution and Secondary Aerosol Formation: A Case Study during the KORUS-AQ Field Campaign

Abstract Clouds and precipitation play critical roles in wet removal of aerosols and soluble gases in the atmosphere, and hence their accurate prediction largely influences accurate prediction of air pollutants. In this study, the impacts of clouds and precipitation on wet scavenging and long-range transboundary transport of pollutants are examined during the 2016 Korea–United States Air Quality (KORUS-AQ) field campaign using the Weather Research and Forecasting Model coupled with chemistry. Two simulations—one in which atmospheric moisture is constrained and one in which it is not—are performed and evaluated against surface and airborne observations. The simulation with moisture constraints is found to better reproduce precipitation as well as surface PM2.5, whereas the areal extent and amount of precipitation are overpredicted in the simulation without moisture constraints. As a results of overpredicted clouds and precipitation and consequently overpredicted wet scavenging, PM2.5 concentration is generally underpredicted across the model domain in the simulation without moisture constraints. The effects are significant not only in the precipitating region (upwind region, southern China in this study) but also in the downwind region (South Korea) where no precipitation is observed. The difference in upwind precipitation by 77% on average between the two simulations leads to the difference in PM2.5 by ∼39% both in the upwind and downwind regions. The transboundary transport of aerosol precursors, especially nitric acid, has a considerable impact on ammonium-nitrate aerosol formation in the ammonia-rich downwind region. This study highlights that skillful prediction of atmospheric moisture can have ultimate potential to skillful prediction of aerosols across regions.

Investigation on the difference of PM2.5 transport flux between the North China Plain and the Sichuan Basin

In this study, an advanced transport flux method based on the comprehensive stereoscopic observation verification was used to evaluate the contribution of transport to pollution in the North China Plain (NCP) and Sichuan Basin (SCB). The simulations were verified using fixed-point lidar and satellite observations, and the results demonstrated that the modeling flux calculation method was suitable for investigating the evolutionary trend of PM2.5 flux. The monthly inflow, outflow, and net flux indicated the intensive interactions and significant temporal and spatial variations of PM2.5 transport though the boundary segment of the city and region. In both autumn and winter, the Beijing-Tianjin-Hebei (BTH) region always played the role of “source” region, while Cheng-Yu (CY) region principally acted as a “convergence” region. In autumn and winter, the BTH region was controlled by a cold front weather system, and the pollutants were readily transported to the surrounding areas. However, the CY region was affected by the complex terrain, with small wind speeds near the ground and poor horizontal diffusion. The upper air was controlled by the northwestern winds behind the trough, and the air subsidence is substantial. The combined action of horizontal and vertical wind directions caused the accumulation of pollutants in the basin. Notably, one intrinsic transport pathway during two representative months was identified based on the investigation of cross-city and cross-region transport, namely the southwest–northeast (SW–NE) pathway in the BTH region. In the CY region, the northeastern transport pathway greatly influenced the cities in the basin. Additionally, through the analysis of the typical heavy pollution processes in the two regions, it was found that the southwest pathway of the BTH region and northeast pathway of the CY region in inter-city transport were significant factors before reaching the most severe stage of pollution.

Air Pollution as a Determinant of Undernutrition Prevalence among Under-Five Children in India: An Exploratory Study.

The association of air pollution with prevalence of undernutrition indices (namely, anaemia, stunting, wasting and underweight) among under-five children in India was investigated. Estimates of population weighted annual average gridded PM2.5 concentrations and proportion of households using solid cooking fuel (HSCF usage percent) during 2017 in India, reflecting the magnitude of ambient and household air pollution respectively, were extracted in a state-wise manner from India State Level Disease Burden Initiative (ISLDBI) reports. Their relationships with the corresponding prevalence of anaemia, underweight, wasting and stunting in under-five children were analysed. The state-level PM2.5 concentrations (mean: 65.5 µg/m3; median: 49.2 µg/m3; range: 17.3-209.0 µg/m3) correlated significantly (P < 0.01) with anaemia (r = 0.65), stunting (r = 0.58) and underweight (r = 0.50) prevalence; while HSCF usage (mean: 49.3%; median: 46.0%; range: 1.9-81.5%) correlated significantly (P < 0.01) with stunting (r = 0.69) and underweight (r = 0.58) prevalence. When examined across median cut-offs and after adjusting for socio-demographic index, the association of anaemia prevalence with PM2.5 concentrations persisted. This association was maintained even after controlling for the coverage of anaemia-specific interventions (namely, iron supplements and deworming medications). The mean difference in PM2.5 concentrations between the high and low PM2.5 states was 58.6 µg/m3, which accounted for 11.8% higher anaemia prevalence in the former as compared to the latter. The burden of childhood undernutrition, particularly anaemia, in India may be linked to PM2.5 levels. To mitigate this burden, it may be necessary to complement the ongoing nutritional interventions with air pollution control measures.

A survey of PM2.5 preventive behavioral intention and related factors among community elderly in Northern Taiwan.

Population aging and air pollution are global concerns. The purpose of this study is to explore the relationship among particulate matter with a diameter of 2.5 μm or less (PM2.5) knowledge, PM2.5 preventive attitude, and PM2.5 preventive behavioral intention in the elderly.A cross-sectional survey design was applied in this study, including usage intention and snowball sampling. A total of 617 elderly people aged over 65 participated, and the collected data were quantitatively analyzed.The results showed that the mean score of PM2.5 knowledge of the elderly was 10.34 (79.53%) with the standard deviation (SD) of 3.42, the mean score of PM2.5 preventive attitude was 4.58 (91.60%) with SD of 0.49, and the mean score of PM2.5 preventive behavioral intention was 4.72 (94.40%) with SD of 0.40. Elderly people's characteristics regarding PM2.5 knowledge and PM2.5 preventive attitude explained 22% (adjusted R2 = 0.22, F = 30.44, P < .001) of the variance in PM2.5 preventive behavioral intention.It is concluded that we found no difference in PM2.5 knowledge, PM2.5 preventive attitude, or PM2.5 preventive behavioral intention among the elderly with or without chronic diseases. In our opinion, health education regarding the threat of PM2.5 to the health of the elderly should be strengthened, to enhance their knowledge, preventive attitude, and preventive behavioral intention of PM2.5.

Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology

Satellite-based rapid sweeping screening of localized PM2.5 hotspots at fine-scale local neighborhood levels is highly desirable. This motivated us to develop a random forest–convolutional neural network–local contrast normalization (RF–CNN–LCN) pipeline that detects local PM2.5 hotspots at a 300 m resolution using satellite imagery and meteorological information. The RF–CNN joint model in the pipeline uses three meteorological variables and daily 3 m/pixel resolution PlanetScope satellite imagery to generate daily 300 m ground-level PM2.5 estimates. The downstream LCN processes the estimated PM2.5 maps to reveal local PM2.5 hotspots. The RF–CNN joint model achieved a low normalized root mean square error for PM2.5 of within ~31% and normalized mean absolute error of within ~19% on the holdout samples in both Delhi and Beijing. The RF–CNN–LCN pipeline reasonably predicts urban PM2.5 local hotspots and coolspots by capturing both the main intra-urban spatial trends in PM2.5 and the local variations in PM2.5 with urban landscape, with local hotspots relating to compact urban spatial structures and coolspots being open areas and green spaces. Based on 20 sampled representative neighborhoods in Delhi, our pipeline revealed an annual average 9.2 ± 4.0 μg m−3 difference in PM2.5 between the local hotspots and coolspots within the same community. In some cases, the differences were much larger; for example, at the Indian Gandhi International Airport, the increase was 20.3 μg m−3 from the coolest spot (the residential area immediately outside the airport) to the hottest spot (airport runway). This work provides a possible means of automatically identifying local PM2.5 hotspots at 300 m in heavily polluted megacities and highlights the potential existence of substantial health inequalities in long-term outdoor PM2.5 exposures even within the same local neighborhoods between local hotspots and coolspots.

Analysis of the Near-road Fine Particulate Exposure to Pedestrians at Varying Heights

ABSTRACT Scientific literature has overlooked how PM2.5 concentrations vary with varying pedestrian heights near a roadway. Understanding this is important because walking is an essential commuting element of a sustainable transportation system, and pedestrians’ height varies widely. Therefore, the focus of the current study is to bridge this gap using results from CALINE 4 model and mobile PM2.5 measurements. In CALINE 4, a simple pedestrian pathway depicting the selected study site located near the Sardar Patel Road, Chennai, India, was simulated. The PM2.5 concentrations were estimated on this pathway at varying heights (0.1–1.8 m) in 135 simulated runs. Subsequently, the sensitivity of the PM2.5 exposure difference across heights was explored with varying ambient PM2.5 concentrations, wind speed, traffic volume, and traffic compositions. Results indicated that the PM2.5 concentrations reduced with increasing heights of pedestrians in all the modelled runs. When this PM2.5 exposure difference was investigated with varying surrounding conditions, it was found that the difference in PM2.5 exposure across heights was influenced by the wind speed, traffic volume, and traffic composition. Ambient PM2.5 concentrations had no discernible effect on it. Car-dominated traffic with a higher mode share of heavy commercial vehicles was marked with the highest PM2.5 exposure difference across heights. For traffic volume, it was observed that for every 100 vehicles hr–1 increase in traffic volume, the PM2.5 exposure difference increased by 0.13 µg m–3 m–1 in the range of pedestrian’s height. For wind speed, calculations suggested that for every 1 m s–1 increase in wind speed, the PM2.5 exposure difference was reduced by 0.095 µg m–3 m–1 in the range of pedestrian’s height. Finally, to bolster the modelling results, mobile PM2.5 measurements (using portable, low-cost optical particle sensors) were conducted near a busy urban roadway at two different heights, 80 cm and 150 cm, during peak and off-peak hours. The results of mobile measurements were found to be consistent with CALINE 4 modelled results.

Role of emission controls in reducing the 2050 climate change penalty for PM2.5 in China

Previous studies demonstrated that global warming can lead to deteriorated air quality even when anthropogenic emissions were kept constant, which has been called a climate change penalty on air quality. It is expected that anthropogenic emissions will decrease significantly in the future considering the aggressive emission control actions in China. However, the dependence of climate change penalty on the choice of emission scenario is still uncertain. To fill this gap, we conducted multiple independent model simulations to investigate the response of PM2.5 to future (2050) climate warming (RCP8.5) in China but with different emission scenarios, including the constant 2015 emissions, the 2050 CLE emissions (based on Current Legislation), and the 2050 MTFR emissions (based on Maximum Technically Feasible Reduction). For each set of emissions, we estimate climate change penalty as the difference in PM2.5 between a pair of simulations with either 2015 or 2050 meteorology. Under 2015 emissions, we find a PM2.5 climate change penalty of 1.43 μg m−3 in Eastern China, leading to an additional 35,000 PM2.5-related premature deaths [95% confidence interval (CI), 21,000-40,000] by 2050. However, the PM2.5 climate change penalty weakens to 0.24 μg m−3 with strict anthropogenic emission controls under the 2050 MTFR emissions, which decreases the associated PM2.5-related deaths to 17,000. The smaller MTFR climate change penalty contributes 14% of the total PM2.5 decrease when both emissions and meteorology are changed from 2015 to 2050, and 24% of total health benefits associated with this PM2.5 decrease in Eastern China. This finding suggests that controlling anthropogenic emissions can effectively reduce the climate change penalty on PM2.5 and its associated premature deaths, even though a climate change penalty still occurs even under MTFR. Strengthened controls on anthropogenic emissions are key to attaining air quality targets and protecting human health in the context of future global climate change.