Annual PM2 Research Articles

Evaluation of the PM2.5 concentrations in South America: Climatological patterns and trend analysis

This study assessed the concentration of particulate matter with an aerodynamic diameter of 2.5 (PM2.5) over South America (SA) based on climatological patterns and trend analysis. This study used monthly PM2.5 data from the Copernicus Atmosphere Monitoring Service (CAMS) at the European Centre for Medium-range Weather Forecasts from 2003 to 2022. The bilinear interpolation method was applied to the data resampling from 0.75° × 0.75°–0.25° × 0.25° spatial resolution, and subsequently, the climatological analysis was performed on seasonal and annual scales of PM2.5 concentrations for the 20-year series. The assessment comprised three main stages: 1) descriptive statistics, 2) seasonal and annual climatology patterns, and 3) trend analysis (Mann-Kendall - MK and Pettitt tests). Results indicated that the largest annual PM2.5 concentrations were found in the Amazon region and northern Paraguay due the El-Niño South Oscillation events (2003–2005, 2007, 2010, 2020, and 2022). The annual average PM2.5 concentrations exceeded the WHO recommended limit (5 μg m−3), ranging from 15.57 μg m−3 (1999) and 22.74 μg m−3 (2007 and 2010). The highest PM2.5 concentrations were observed in the Amazon and Chile (Santiago), reaching 160.4 μg m−3 annually and 177.8 μg m−3 seasonally (spring). Elevated seasonal PM2.5 concentrations were attributed to increased deforestation and wildfires associated with meteorological systems such as the Bolivian high and subtropical jets during dry periods (winter and spring). The MK test revealed a significant reduction in PM2.5 concentrations between 2003 and 2022, observed in Argentina (north), Brazil (arc of reforestation), Bolivia (south), and Chile (north). Reduction values varied between 0.5 and 10 μg m−3.year−1 annually and during winter, attributed to no-tillage practices in regions cultivating commodities. The Pettitt test also identified significant structural changes occurring in 2006–2008 and 2010–2011, aligning with areas highlighted in the MK test. This study provides valuable insights for air quality and environmental monitoring managers to mitigate PM2.5 pollution in SA.

Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China

Fine particulate matter (PM2.5) and surface ozone air pollution have severe health consequences. Since China implemented the nationwide Air Pollution Prevention and Control Action Plan (APPCAP) in 2013, annual average PM2.5 concentrations have decreased while O3 concentration have increased in Beijing. It is unclear, however, to the extent that short-term meteorological variability has influenced these trends. To separate the influence of meteorology from long-term emissions-induced changes, we quantify the portion of the long-term PM2.5 and O3 concentration signals associated with short-term meteorological variability. Building off recent literature highlighting the regional nature of pollutant variability, we incorporate both locally observed meteorology and average regional reanalysis. We isolated sub-seasonal variability using the Kolmogorov-Zurbenko filter. Next, we trained and compared the utility of three statistical models of varying complexity (a linear model, a general additive model with splines, and a random forest) in their ability to predict out-of-sample daily concentrations. The random forest model yields the best predictive capability in holdout tests and predicts changing meteorological contributions to PM2.5 and ozone concurrent with changing concentrations across the study period. Results show an overall decrease in meteorology induced PM2.5 concentration variability at daily scales since 2013, but variability from meteorology has increased relative to mean PM2.5 concentrations. In contrast, O3 shows the opposite trend, with increasing meteorology-induced variability (meteorology-induced variability normalized by mean observed O3 has decreased). Our results show the importance of including regional meteorology in explaining local PM2.5 and O3 variability and quantify links between long-term policy implementation and short-term meteorological contributions to pollutant concentrations.

Unravelling the impacts of stratospheric intrusions on near-surface ozone during the springtime ozone pollution episodes in Lhasa, China

While air pollution due to fine particulate matter (PM2.5) has been effectively controlled in China; the photochemical pollution characterized by elevated ozone (O3) has emerged as a major concern for air quality improvement. Except for ozone, Lhasa is one of the cleanest cities in China with the lowest annual PM2.5 concentration in 2017. The levels of major air pollutants in Lhasa are much lower than those of other cities in the eastern region of China, especially in May, when the O3 concentration peaks. This study was based on multi-source observations in combination with the Goddard Earth Observing System coupled with chemistry (GEOS-Chem) and the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to explore the causes of O3 pollution in Lhasa in May 2023. The results indicated that during the high O3 episodes, the concentrations of O3 precursors were low in Lhasa. Surrounding cities in other parts of the Tibetan Plateau also experienced high ozone concentrations despite being in different airsheds, suggesting that the O3 pollution in Lhasa was caused by regional transport rather than purely local emissions. Stratospheric intrusion events modulated by the westerly jet led to elevated ozone in the troposphere above Lhasa City. The results of the GEOS-Chem model indicated that horizontal advection, turbulence, diffusion, and other effects led to high concentrations of ozone in the near-surface above Lhasa. Vertical transport was the dominant factor leading to ozone concentration increases during high ozone days, with a contribution of 6.33 Gg/day. In addition, high-altitude air masses with a maximum altitude of over 8000 m, were observed arriving in Lhasa during the high ozone days. This study revealed that stratospheric intrusions have a greater contribution to the high O3 concentration in Lhasa in spring and provided a scientific basis for mitigating O3 pollution in the plateau cities.

Ambient Air Pollution and Depressed Mood in the National Longitudinal Study of Adolescent to Adult Health (Add Health) Wave IV.

Depression is a major contributor to the global burden of disease. There is limited understanding of how environmental exposures may contribute to depression etiology. We used Wave IV of the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine associations between low-level ambient air pollution exposure and depressed mood in a generally healthy population of over 10,000 24-32 year olds. Annual mean PM2.5 levels in the 2008-2009 study were close to the current U.S. standard. In fully adjusted quasi-binomial logistic regression models, there were no meaningful associations between IQR increases in air pollutant and change in depressed mood status regardless of specific pollutant or moving average lags. In interaction effects models, an IQR increase in lag day 0-30 PM2.5 resulted in 1.20 (95% CI, 1.02-1.41) times higher likelihood of having depressed mood, but only for persons with chronic lung disease (interaction P=0.04); the association was null for participants without chronic lung disease (OR 0.98, 95% CI, 0.91, 1.05). Our findings suggest that among persons with a lifetime history of chronic lung disease, greater exposure to even low-level PM2.5, PM10, and sulfate may be associated with modest increases in the likelihood of having depressed mood.

Neighbourhood environments and cognitive health in the longitudinal Personality and Total Health (PATH) through life study: A 12-year follow-up of older Australians

BackgroundUrban neighbourhood environments may impact older adults’ cognitive health. However, longitudinal studies examining key environmental correlates of cognitive health are lacking. We estimated cross-sectional and longitudinal associations of neighbourhood built and natural environments and ambient air pollution with multiple cognitive health outcomes in Australian urban dwellers aged 60+ years. MethodsThe study included 1160 participants of the PATH Through Life study (60+ cohort) who were followed up for 12 years (four assessments; 2001/02 to 2013/15) and with data on socio-demographics, health, cognitive functions and diagnoses, and full residential address. Neighbourhood environmental features encompassed population and street-intersection densities, non-commercial land use mix, transit points, presence of blue space, percentages of commercial land, parkland and tree cover, and annual average PM2.5 and NO2 concentrations. All exposures except for tree cover were assessed at two time points. Generalised additive mixed models estimated associations of person-level average, and within-person changes in, exposures with cognitive functions. Multi-state hidden Markov models estimated the associations of neighbourhood attributes with transitions to/from mild cognitive impairment (MCI). ResultsDense, destination-rich neighbourhoods were associated with a lower likelihood of transition to MCI and reversal to no MCI. Positive cross-sectional and longitudinal associations of non-commercial land use mix, street intersection density and percentage of commercial land were observed especially with global cognition and processing speed. While access to parkland and blue spaces were associated with a lower risk of transition to MCI, the findings related to cognitive functions were mixed and supportive of an effect of parkland on verbal memory only. Higher levels of PM2.5 and NO2 were consistently associated with steeper declines and/or decreases in cognitive functions and worse cognitive states across time. ConclusionTo support cognitive health in ageing populations, neighbourhoods need to provide an optimal mix of environmental complexity, destinations and access to the natural environment and, at the same time, minimise ambient air pollution.

Long-term exposure to air pollution at residential and workplace addresses and breast cancer risk: A case-control study nested in the French E3N-Générations cohort from 1990 to 2011

BackgroundAn increasing evidence links air pollution to breast cancer (BC) risk. Yet, pollutant exposure estimates at the workplace location in pollution exposure assessment have not been considered. ObjectivesThis study investigates the association between particulate matters (PM2·5, PM10) and nitrogen dioxide (NO2) atmospheric concentrations (1990–2011), at the women’s residential and workplace locations, and BC risk. MethodsThis case-control study of 2419 BC cases and 2984 controls, was nested in the French prospective E3N cohort. The annual mean PM2·5, PM10 and NO2 concentrations were estimated using a Land Use Regression model (50 m x 50 m resolution) and assigned to the women’s geocoded residential and workplace locations, from cohort recruitment to their index date (date of case diagnosis). Odds ratios (OR) and 95 % confidence intervals (CI) were estimated using multivariate logistic regression models. ResultsAn increased BC risk was observed for a 10 µg/m3 increase of the 1990–2011 average PM2·5 concentration estimates (OR=1·28; CI 1·00, 1·63). An increased risk was suggested for a 10 µg/m3 increase for PM10 (OR=1·09; CI 0·92, 1·30) and NO2 (OR=1·05; CI 0·97, 1·13). No effect modification by menopausal status, nor difference by hormone receptor status were observed. DiscussionThis study is the first to estimate BC risk and long-term air pollutant exposure from both, residential and workplace location histories. Results suggest that residential PM2·5, PM10 and NO2 concentrations are strongly correlated with workplace ones, indicating that residential data may serve as proxy for overall exposure. Future studies should consider exposure during commuting.

Contribution of ethnicity, area level deprivation and air pollution to paediatric intensive care unit admissions in the United Kingdom 2008–2021

There is emerging evidence on the impact of social and environmental determinants of health on paediatric intensive care unit (PICU) admissions and outcomes. We analysed UK paediatric intensive care data to explore disparities in the incidence of admission according to a child's ethnicity and the degree of deprivation and pollution in the child's residential area. Data were extracted on children <16 years admitted to UK PICUs between 1st January 2008 and 31st December 2021 from the Paediatric Intensive Care Audit Network (PICANet) database. Ethnicity was categorised as White, Asian, Black, Mixed or Other. Deprivation was quantified using the 'children in low-income families' measure and outdoor air pollution was characterised using mean annual PM2.5 level at local authority level, both divided into population-weighted quintiles. UK population estimates were used to calculate crude incidence of PICU admission. Incidence rate ratios were calculated using Poisson regression models. There were 245,099 admissions, of which 60.7% were unplanned. After adjusting for age and sex, Asian and Black children had higher relative incidence of unplanned PICU admission compared to White (IRR 1.29 [95% CI: 1.25-1.33] and 1.50 [95% CI: 1.44-1.56] respectively), but there was no evidence of increased incidence of planned admission. Children living in the most deprived quintile had 1.50 times the incidence of admission in the least deprived quintile (95% CI: 1.46-1.54). There were higher crude admission levels of children living in the most polluted quintile compared to the least (157.8 vs 113.6 admissions per 100,000 child years), but after adjustment for ethnicity, deprivation, age and sex there was no association between pollution and PICU admission (IRR 1.00 [95% CI: 1.00-1.00] per 1μg/m3 increase). Ethnicity and deprivation impact the incidence of PICU admission. When restricting to unplanned respiratory admissions and ventilated patients only, increasing pollution level was associated with increased incidence of PICU admission. It is essential to act to reduce these observed disparities, further work is needed to understand mechanisms behind these findings and how they relate to outcomes. There was no direct funding for this project. HM was funded by an NIHR Academic Clinical Fellowship (ACF-2022-18-017).

The Association between Long-term PM2.5 Exposure and Risk for Pancreatic Cancer: An Application of Social Informatics.

There is a profound need to identify modifiable risk factors to screen and prevent pancreatic cancer. Air pollution, including fine particulate matter (PM2.5), is increasingly recognized as a risk factor for cancer. We conducted a case-control study using data from the electronic health record (EHR) of Duke University Health System, 15-year residential history, NASA satellite fine particulate matter (PM2.5) and neighborhood socioeconomic data. Using deterministic and probabilistic linkage algorithms, we linked residential history and EHR data to quantify long term PM2.5 exposure. Logistic regression models quantified the association between a one interquartile range (IQR) increase in PM2.5 concentration and pancreatic cancer risk. The study included 203 cases and 5027 controls (median age of 59 years, 62% female, 26% Black). Individuals with pancreatic cancer had higher average annual exposure (9.4 μg/m3) as compared to IQR increase in average annual PM2.5 was associated with greater odds of pancreatic cancer (OR=1.20; 95% CI: 1.00-1.44). These findings highlight the link between elevated PM2.5 exposure and increased pancreatic cancer risk. They may inform screening strategies for high-risk populations and guide air pollution policies to mitigate exposure.

Ambient air pollution and survival in childhood cancer: A nationwide survival analysis.

Particulate matter consisting of fine particles measuring 2.5 microns or less in diameter (PM2.5), a component of air pollution, has been linked to adverse health outcomes. The objective of this study was to assess the association between ambient PM2.5 exposure and survival in children with cancer in the United States. Individuals aged birth to 19 years who were diagnosed with cancer between January 1, 2004, and December 31, 2019, were selected from the National Cancer Database. The association between the annual PM2.5 level at the patient's zip code of residence at the time of diagnosis and overall survival was evaluated using time-varying Cox proportional hazards models (crude and adjusted for diagnosis year and age). To address concerns that exposure to air pollution is correlated with other social determinants of health, the authors tested the association between PM2.5 levels and survival among sociodemographic subgroups. Of the 172,550 patients included, 27,456 (15.9%) resided in areas with annual PM2.5 concentrations above the US Environmental Protection Agency (EPA) annual PM2.5 standard of 12 μg/m3. Residing in these high-pollution areas was associated with worse overall survival (adjusted hazard ratio [aHR], 1.06; 95% confidence interval [CI], 1.012-1.10). Similarly, when PM2.5 was evaluated as a linear measure, each unit increase in PM2.5 exposure was associated with worse survival (aHR, 1.011; CI, 1.005-1.017). Exposure to PM2.5 at levels above the EPA standards was also significantly associated with worse overall survival among sociodemographic subgroups. Exposure to PM2.5 was significantly associated with worse overall survival among children with cancer, even at levels below EPA air quality standards. These results underscore the importance of setting appropriate air quality standards to protect the health of this sensitive population. The authors investigated how living in areas with high air pollution (defined as particulate matter consisting of fine particles measuring 2.5 microns or less in diameter; PM2.5) affects the overall survival of children with cancer in the United States. The results indicated that children living in areas with higher PM2.5 levels, and even at levels below prior and current US Environmental Protection Agency standards, had lower survival rates than children living in areas with lower levels of PM2.5. This finding emphasizes the need for stricter air quality standards to better protect children, particularly those with serious health conditions like childhood cancer.

PM2.5 constituents associated with mortality and kidney failure in childhood-onset lupus nephritis: A 19-year cohort study

BackgroundChildhood-onset lupus nephritis (cLN) is a severe form of systemic lupus erythematosus (SLE) with high morbidity and mortality. The impact of long-term exposure to fine particulate matter (PM2.5) on adverse outcomes in cLN remains unclear. MethodsWe combined a 19-years cLN cohort from seven provinces in China with high-resolution PM2.5 dataset from 2001 to 2020, investigating the association between long-term exposure to PM2.5 and its constituents (sulfate, nitrate, organic matter, black carbon, ammonium) with the risk of death and kidney failure, analyzed with multiple variables Cox models. We also evaluated the association between 3-year average PM2.5 exposure before study entry and baseline SLE disease activity index (SLEDAI) scores using linear regression models. ResultsEach 10 μg/m3 increase in annual average PM2.5 exposure was associated with an increased risk of death and kidney failure (HR = 1.58, 95 % CI: 1.24–2.02). Black carbon showed the strongest association (HR = 2.14, 95 % CI: 1.47–3.12). Higher 3-year average exposures to PM2.5 and its constituents were significantly associated with higher baseline SLEDAI scores. ConclusionsThese findings highlight the significant role of environmental pollutants in cLN progression and emphasize the need for strategies to mitigate exposure to harmful PM2.5 constituents, particularly in vulnerable pediatric populations.

Optimization of a PM2.5 automatic monitoring network based on the modeling approach – A case study in Long An province, Vietnam

Abstract Currently, fine particulate matter (PM2.5) pollution is one of the well-recognized serious environmental health risks. In particular, PM2.5 has remarkably impacted the human respiratory, cardiovascular, and circulatory systems. Vietnam has frequently ranked among the countries with the highest annual mean PM2.5 levels in Southeast Asia and worldwide. Thus, building a real-time continuous ambient PM2.5 monitoring network is an important environmental management tool to reduce health effects attributable to PM2.5 exposure. Nevertheless, measurement locations of an air quality monitoring (AQM) network depend strongly on meteorological conditions in the area and sensitivity to local primary emission sources, which significantly determines the AQM network’s operational efficiency. This study aims to estimate and optimize outdoor PM2.5 monitoring sites of an automatic air quality monitoring (AAQM) network using a system of low-cost sensors in Long An province, based on the spatio-temporal distribution assessment results of PM2.5 concentrations from WRF (Weather Research and Forecasting Model)/CMAQ (the Community Multiscale Air Quality) models. The prominent study outcomes have shown that the 2018 average PM2.5 concentration in Long An province exceeded the threshold of QCVN 05:2023/BTNMT (25 μg/m3) from 5.6 to 7.9 times. Moreover, this study has proposed four optimal monitoring sites (AQ1, AQ2, AQ3, and AQ4) for the automatically real-time PM2.5 observation in the areas of Tan An City, Can Giuoc District, the ring roads 3 and 4, and Kien Tuong Town. The study has provided an important scientific basis and support for local air quality management activities; furthermore, the newly proposed monitoring locations will complement the province’s periodic monitoring program towards 2025.

The impacts of reduction in ambient fine particulate air pollution on natural-cause mortality in Taiwan

ABSTRACT Many epidemiologic studies have reported an association between high concentrations of fine particulate matter (PM2.5) and increased mortality rates. Concurrently an association between decreased concentration of these airborne PM2.5 pollutants and a decline in mortality frequency was noted in certain investigations globally; however, only a very few of these studies were conducted in Asia. Taiwan was found to exhibit a 30% decline in ambient PM2.5 levels over the last 20 years. The aim of this ecological investigation was to examine the contribution of annual reductions in ambient PM2.5 to changes in age-standardized natural-cause mortality rates (ASRs) in 65 townships in Taiwan from 2006 to 2020 controlling for lung cancer mortality rate, physician density, and annual household income. Data demonstrated a 0.9/105 fall in adjusted ASR for every 10 ug/m3 reduction in mean annual PM2.5 level in Taiwan during this 14-year period, suggesting a significant association between reductions in ambient PM2.5 levels and decreases in natural-cause mortality rates.

Long-term measurements of aerosol composition at rural background sites in France: Sources, seasonality and mass closure of PM2.5

Fine particulate matter (PM2.5) was monitored at five rural background locations in France from 2012 to 2021. Annual PM2.5 ranged from 5 to 15 μg m−3, all sites repeatedly exceeding the 2021 annual World Health Organization guideline. Chemical speciation including organic and elemental carbon (OC, EC), secondary inorganic aerosols (SIA: NO3−, SO42−, NH4+) and other major water-soluble ions (Cl−, Na+, Mg2+, Ca2+, K+) were monitored on 24-h filters covering 14% of the year. A source apportionment of OM was undertaken based on fine potassium and using representative ratios for domestic biomass burning (BB) and fossil fuel (FF) emissions. The latter dominated the EC fraction (55–60%) while BB was the main primary source of OM (27–54%). The average mass balance of PM2.5 at the French rural background atmospheric sites was: secondary organic aerosol (18–35%), BB (17–27%), non-sea-salt sulphate (12–17%), nitrate (6–22%), ammonium (7–11%), FF (4–6%), mineral dust (3–9%) and sea salt (1–2%). Secondary aerosols were the main component of PM2.5 through all seasons, with SIA dominating in spring episodes. The contribution of OM to PM2.5 was larger at the southern sites whereas the contribution of SIA was larger at the northern sites. The mean OC/EC ratio and the good correlations between OC, EC, and fine potassium suggested that BB was the main primary source contributing to carbonaceous aerosols, 27–54% to total OM depending on the site; and also, to PM2.5 (17–27%). Stronger regulations of OM sources including BB, nitrate from combustion, and ammonium from agricultural sources are needed to reduce PM2.5 at rural background sites on an annual and episodic basis, respectively.

Drivers of PM2.5 Episodes and Exceedance in India: A Synthesis From the COALESCE Network

AbstractEmission sources influencing high particulate air pollution levels and related mortality in India have been studied earlier on country‐wide and sub‐national scales. Here, we use novel data sets of emissions (for 2019) and observations created under the Carbonaceous Aerosol Emissions, Source Apportionment, and Climate Impacts network in India (Venkataraman et al., 2020, https://doi.org/10.1175/bams‐d‐19‐0030.1) in WRF‐Chem simulations to evaluate drivers of high PM2.5 levels during episodes and in airsheds with different pollution levels. We identify airsheds in “extreme” (110–140 μg/m3), “severe” (80–110 μg/m3) and “significant” (40–80 μg/m3) exceedance of the Indian annual ambient air quality standard (National Ambient Air Quality Standards [NAAQS]) of 40 μg/m3 for PM2.5. We find that primary organic matter and anthropogenic mineral matter (largely coal fly‐ash) drive high PM2.5 levels, both annually and during high PM2.5 episodes. PM2.5 episodes are driven by organic aerosol in north India (Mohali) in wintertime but are additionally influenced by mineral matter and secondary inorganics in central (Bhopal), south India (Mysuru) and eastern India (Shyamnagar). Across airsheds in exceedance of the NAAQS and during high PM2.5 episodes, primary PM2.5 emissions arise largely from the residential sector (50%–75%). Formal sector emissions (industry, thermal power and transport; 40%–55%) drive airshed and episode scale PM2.5 exceedance in northern and eastern India. Agricultural residue burning emissions predominate (50%–75%) on episode scales, both in northern and central India, but not on annual scales. Interestingly, residential sector emissions strongly influence (60%–90%) airsheds in compliance with the NAAQS (annual mean PM2.5 &lt; 40 μg/m3), implying the need for modern residential energy transitions for the reduction of ambient air pollution across India.

Spatiotemporal Trends and Driving Factors of PM2.5-related Health Burden in Gansu Province, China from 2013 to 2020

To evaluate the spatiotemporal trends and drivers of PM2.5-related health effects in Gansu Province since the implementation of the Air Pollution Prevention and Control Action Plan, the latest global exposure mortality model （GEMM） was adopted to estimate the health burden attributable to PM2.5 in Gansu Province from 2013 to 2020. The factor decomposition method was used to further quantify the main causes of the long-term changes in deaths attributable to PM2.5 pollution. The results showed that from 2013 to 2020, the population-weighted PM2.5 concentration in Gansu Province decreased by 34.57%, and the proportion of people exposed to areas with an annual average PM2.5 concentration exceeding 35 μg·m-3 decreased significantly from 72.89% to 11.61%. Moreover, the number of attributable deaths in Gansu Province declined from 12 826 （95%CI： 7 840-17 408） in 2 013 to 9 814 （95%CI： 6 407-13 036） in 2020, indicating a decrease of 23.48%. Attributable deaths from stroke, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infection declined, whereas deaths from ischemic heart disease increased by 12.11%. Notably, individuals aged 60 years and older accounted for more than 80% of all age-related deaths. The number of deaths attributable to PM2.5 in central and eastern Gansu Province was significantly higher than that in the Hexi region, and most regions showed a downward trend. The contribution of the total population, age structure, baseline mortality rate, and PM2.5 concentration to the change in PM2.5-related deaths was -1.26%, 16.16%, -9.84%, and -28.55%, respectively. Overall, population aging and a decrease in PM2.5 concentration were the main factors contributing to the increase and decrease in PM2.5-related deaths, respectively. The active clean air policies in Gansu Province have reduced the health burden caused by PM2.5 pollution, but with the trend of population aging, a significant reduction in PM2.5 concentration will be needed in the future to avoid more attributable deaths.

Associations of PM2.5 exposure with emergency department visits and readmissions among preterm infants with bronchopulmonary dysplasia.

To quantify the association of ambient air pollution (particulate matter, PM2.5) exposure with medically attended acute respiratory illness among infants with bronchopulmonary dysplasia (BPD). Single center, retrospective cohort study of preterm infants with BPD in Metropolitan Philadelphia. Multivariable logistic regression quantified associations of annual mean PM2.5 exposure (per μg/m3) at the census block group level with medically attended acute respiratory illness, defined as emergency department (ED) visits or hospital readmissions within a year after first hospital discharge adjusting for age at neonatal intensive care unit (NICU) discharge, year, sex, race, insurance, BPD severity, and census tract deprivation. As a secondary analysis, we examined whether BPD severity modified the associations. Of the 378 infants included in the analysis, 189 were non-Hispanic Black and 235 were publicly insured. Census block PM2.5 level was not significantly associated with medically attended acute respiratory illnesses, ED visits, or hospital readmissions in the full study cohort. We observed significant effect modification by BPD grade; each 1 µg/m3 higher annual PM2.5 exposure was medically attended acute respiratory illness (adjusted odds ratio [aOR] 1.65, 95% CI: 1.06-2.63) among infants with Grade 1 BPD but not among infants with grade 3 BPD (aOR 0.83, 95% CI: 0.47-1.48) (interaction p = .024). Cumulative PM2.5 exposure in the year after NICU discharge was not significantly associated with medically attended acute respiratory illness among infants with BPD. However, infants with Grade 1 BPD had significantly higher odds with higher exposures. If replicated, these findings could inform anticipatory guidance for families of these infants to avoid outdoor activities during high pollution days after NICU discharge.

Particulate Matter Exposure and Default Mode Network Equilibrium During Early Adolescence.

Background: Air pollution exposure has been associated with adverse cognitive and mental health outcomes in children, adolescents, and adults, although youth may be particularly susceptible given ongoing brain development. However, the neurodevelopmental mechanisms underlying the associations among air pollution, cognition, and mental health remain unclear. We examined the impact of particulate matter (PM2.5) on resting-state functional connectivity (rsFC) of the default mode network (DMN) and three key attention networks: dorsal attention, ventral attention, and cingulo-opercular. Methods: Longitudinal changes in rsFC within/between networks were assessed from baseline (9-10 years) to the 2-year follow-up (11-12 years) in 10,072 youth (M ± SD = 9.93 + 0.63 years; 49% female) from the Adolescent Brain Cognitive Development (ABCD®) study. Annual ambient PM2.5 concentrations from the 2016 calendar year were estimated using hybrid ensemble spatiotemporal models. RsFC was estimated using functional neuroimaging. Linear mixed models were used to test associations between PM2.5 and change in rsFC over time while adjusting for relevant covariates (e.g., age, sex, race/ethnicity, parental education, and family income) and other air pollutants (O3, NO2). Results: A PM2.5 × time interaction was significant for within-network rsFC of the DMN such that higher PM2.5 concentrations were associated with a smaller increase in rsFC over time. Further, significant PM2.5 × time interactions were observed for between-network rsFC of the DMN and all three attention networks, with varied directionality. Conclusion: PM2.5 exposure was associated with alterations in the development and equilibrium of the DMN-a network implicated in self-referential processing-and anticorrelated attention networks, which may impact trajectories of cognitive and mental health symptoms across adolescence.

What can we learn from nested IoT low‐cost sensor networks for air quality? A case study of PM2.5 in Birmingham, UK

AbstractLow‐cost sensing and the Internet of Things (IoT), present new possibilities for unconventional monitoring of environmental parameters. This paper describes a series of intersecting networks of particulate matter sensors that were deployed across the Birmingham conurbation for a 12‐month period. The networks consisted of a combination of commercially available sensors and University developed sensors. Data from these networks were assimilated with data from a third‐party Zephyr deployment, along with the DEFRA AURN network, which was hosted on an open‐source online platform. This nesting of sensor networks allowed for new insights into sensor performance, including the accuracy of a large network to detect regional concentrations and the number of sensors needed for effective monitoring beyond indicative measurements. After comprehensive data validation steps, the sensors were shown to perform well during co‐location with reference instrumentation (exhibiting slopes of 0.74–1.3). The sensors demonstrated good capability of detecting temporal patterns of regional PM2.5 with the mean of the entire sensor network recording an annual mean PM2.5 concentration within 0.2 μgm−3 of the regulatory network annual mean observation. Network‐derived statistics for estimating urban background concentrations compared to a reference site increase in‐line with the number of sensors available, however when assessing this for near‐source concentrations the importance of sensor location rather than the number of sensors is highlighted. Overall, the network provided novel insights into local concentrations, detecting similar hotspots to those identified by a high‐resolution model. The increased spatial coverage afforded by the sensor network has the potential to support higher resolution evaluation of models and provide unprecedented spatial evidence for air pollution management interventions.

Longitudinal Analysis of Annual PM2.5 Concentration Variations in Blantyre City, Malawi

This study presents current levels of fine particulate matter, with aerodynamic diameter of less than 2.5 micrometre (PM2.5) in the city of Blantyre, Malawi measured between June 2021 to May 2022. PM2.5 measurements were done in 18 different locations (spanning greater than 2 km apart) using Dylos DC1100 PRO Laser Particle Counter (2018 model). The sampling points were; 3 school campuses, 3 hospitals, 3 industrial areas, 3 open markets, 3 residential areas, and 3 commercial/ business centres (CBC) of Blantyre. PM2.5 monitoring was conducted between 10:00-12:00 hours and 15:00-17:00 hours local time. The results showed that the hourly mean PM2.5 concentrations (µg/m3) for the 10:00-12:00 time period were; 43 ± 23 µg/m3 for school campuses, 35 ± 16 µg/m3 for hospitals, 62 ± 38 µg/m3 for industrial areas, 44 ± 26 µg/m3 for markets, 40 ± 21 µg/m3 for residential areas and 35 ± 16 µg/m3 for CBC. The results showed that the hourly mean PM2.5 concentrations (µg/m3) for the 15:00-17:00 time period were; 38 ± 22 µg/m3 for school campuses, 34 ± 18 µg/m3 for hospitals, 57 ± 37 µg/m3 for industrial areas, 42 ± 25 µg/m3 for markets, 36 ± 23 µg/m3 for residential areas and 34 ± 18 µg/m3 for CBC. Significant increases of PM2.5 levels were observed in school campuses, residential areas and CBC during the months of June-October, which are windy and drier. On the other hand, lowest concentration of PM2.5 was observed during the warm season (November-March) across the sampling locations over Blantyre. Based on these findings, this study recommends further investigation of long-term concentration of PM2.5 in Blantyre city because it is hazardous and likely to cause health implications to the local population. Furthermore, interventions should be sought to reduce PM2.5 concentration in the city.

Exposures to particulate matters and childhood sleep disorders—A large study in three provinces in China

ObjectivesEvidence on the link between long-term ambient particulate matter (PM) exposures and childhood sleep disorders were scarce. We examined the associations between long-term exposures to PM2.5 and PM1 (PM with an aerodynamic equivalent diameter <2.5 μm and <1 μm, respectively) with sleep disorders in children. MethodsWe performed a population-based cross-sectional survey in 177,263 children aged 6 to 18 years in 14 Chinese cities during 2012–2018. A satellite-based spatiotemporal model was employed to estimate four-year annual average PM2.5 and PM1 exposures at residential and school addresses. Parents or guardians completed a checklist using the Sleep Disturbance Scale for Children. We estimated the associations using generalized linear mixed models with adjustment for characteristics of children, parents, and indoor environments. ResultsLong-term PM2.5 and PM1 exposures were positively associated with odds of sleep disorders for almost all domains. For example, increments in PM2.5 and PM1 per 10 μg/m3 were associated with odds ratios of global sleep disorder of 1.24 (95 % confidence interval [CI]: 1.14, 1.35) and 1.31 (95 %CI: 1.18, 1.46), respectively. Similar results were observed for subtypes of sleep disorder. These associations were heterogeneous regionally, with stronger associations among children residing in southeast region than in northeast and northwest regions. Moreover, larger estimates of PM1 were found than that of PM2.5 in southeast region. ConclusionLong-term PM2.5 and PM1 exposures are independently associated with higher risks of childhood sleep disorders, and these associations vary by geographical region.