Ambient Air Pollutant Concentrations Research Articles

Delayed effects of air pollution on public bike-sharing system use in Seoul, South Korea: A time series analysis

Background:As a complementary means to urban public transit systems, public bike-sharing provides a green and active mode of sustainable mobility, while reducing carbon-dioxide emissions and promoting health. There has been increasing interest in factors affecting bike-sharing usage, but little is known about the effect of ambient air pollution. Method:To assess the short-term impact of daily exposure to multiple air pollutants (PM2.5, PM10, NO2, and O3) on the public bike-sharing system (PBS) usage in Seoul, South Korea (2018–2021), we applied a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model (DLNM). The model was adjusted for day of the week, holiday, temperature, relative humidity, and long-term trend. We also conducted stratification analyses to examine the potential effect modification by age group, seasonality, and COVID-19. Results:We found that there was a negative association between daily ambient air pollution and the PBS usage level at a single lag day 1 (i.e., air quality a day before the event) across all four pollutants. Our results suggest that days with high levels of air pollutants (at 95th percentile) are associated with a 0.91% (0.86% to 0.96%) for PM2.5, 0.89% (0.85% to 0.94%) for PM10, 0.87% (0.82% to 0.91%) for O3, and 0.92% (0.87% to 0.98%) for NO2, reduction in cycling behavior in the next day compared to days with low levels of pollutants (at 25th percentile). No evidence of effect modification was found by seasonality, age nor the COVID-19 pandemic for any of the four pollutants. Conclusions:Our findings suggest that high concentrations of ambient air pollution are associated with decreased rates of PBS usage on the subsequent day regardless of the type of air pollutant measured.

Air pollution emission inventory using national high-resolution spatial parameters for the Nordic countries and analysis of PM2.5 spatial distribution for road transport and machinery and off-road sectors

Abstract. Air pollution is an important cause of adverse health effects, even in the Nordic countries, which have relatively good air quality. Modelling-based air quality assessment of the health impacts relies on reliable model estimates of ambient air pollution concentrations, which furthermore rely on good-quality spatially resolved emission data. While quantitative emission estimates are the cornerstone of good emission data, description of the spatial distribution of the emissions is especially important for local air quality modelling at high resolution. In this paper we present a new air pollution emission inventory for the Nordic countries with high-resolution spatial allocation (1 km × 1 km) covering the years 1990, 1995, 2000, 2005, 2010, 2012, and 2014. The inventory is available at https://doi.org/10.5281/zenodo.10571094 (Paunu et al., 2023). To study the impact of applying national data and methods to the spatial distribution of the emissions, we compared road transport and machinery and off-road sectors to CAMS-REGv4.2, which used a consistent spatial distribution method throughout Europe for each sector. Road transport is a sector with well-established proxies for spatial distribution, while for the machinery and off-road sector, the choice of proxies is not as straightforward as it includes a variety of different type of vehicles and machines operating in various environments. We found that CAMS-REGv4.2 was able to produce similar spatial patterns to our Nordic inventory for the selected sectors. However, the resolution of our Nordic inventory allows for more detailed impact assessment than CAMS-REGv4.2, which had a resolution of 0.1° × 0.05° (longitude–latitude, roughly 5.5 km × 3.5–6.5 km in the Nordic countries). The EMEP/EEA Guidebook chapter on spatial mapping of emissions has recommendations for the sectoral proxies. Based on our analysis we argue that the guidebook should have separate recommendations for proxies for several sub-categories of the machinery and off-road sectors, instead of including them within broader sectors. We suggest that land use data are the best starting point for proxies for many of the subsectors, and they can be combined with other suitable data to enhance the spatial distribution. For road transport, measured traffic flow data should be utilized where possible, to support modelled data in the proxies.

Incidence of central retinal artery occlusion peaks in winter season.

Stroke incidence exhibits seasonal trends, with the highest occurrences observed during winter. This study investigates the incidence of central retinal artery occlusion (CRAO), a stroke equivalent of the retina, and explores its monthly and seasonal variations, as well as potential associations with weather and ambient air pollutants. A retrospective search of medical records spanning 15 years (January 2008-December 2022) was conducted at the University Eye Hospital Tübingen, Germany, focusing on diagnosed cases of CRAO. Incidences were evaluated on a monthly and seasonal basis (winter, spring, summer, fall). Weather data (temperature, precipitation, atmospheric pressure) and concentrations of ambient air pollutants [fine particulate matter (PM2.5), coarse particulate matter (PM10), nitrogen dioxide (NO2), and ozone (O3)], were analyzed for a potential association with CRAO incidence. Out of 432 patients diagnosed with CRAO between 2008 and 2022, significantly varying incidences were observed monthly (p = 0.025) and seasonally (p = 0.008). The highest rates were recorded in February and winter, with the lowest rates in June and summer. Concentrations of NO2, PM2.5 and lower ambient air temperature (average, minimum, maximum) showed significant correlations with CRAO incidence. This comprehensive 15-year analysis reveals a pronounced winter peak in CRAO incidence, with the lowest occurrences in summer. Potential associations between CRAO incidence and ambient air pollutants and temperature underscore the importance of considering seasonal trends and call for further investigations to elucidate contributing factors, potentially leading to targeted preventive strategies and public health interventions.

Black carbon particles in human breast milk: assessing infant's exposure.

Human breast milk is the recommended source of nutrition for infants due to its complex composition and numerous benefits, including a decline in infection rates in childhood and a lower risk of obesity. Hence, it is crucial that environmental pollutants in human breast milk are minimized. Exposure to black carbon (BC) particles has adverse effects on health; therefore, this pilot study investigates the presence of these particles in human breast milk. BC particles from ambient exposure were measured in eight human breast milk samples using a white light generation under femtosecond illumination. The carbonaceous nature of the particles was confirmed with BC fingerprinting. Ambient air pollution exposures (PM2.5, PM10, and NO2) were estimated using a spatial interpolation model based on the maternal residential address. Spearman rank correlation coefficients were obtained to assess the association between human breast milk's BC load and ambient air pollution exposure. BC particles were found in all human breast milk samples. BC loads in human breast milk were strongly and positively correlated with recent (i.e., 1 week) maternal residential NO2 (r = 0.79; p = 0.02) exposure and medium-term (i.e., 1 month) PM2.5 (r = 0.83; p = 0.02) and PM10 (r = 0.93; p = 0.002) exposure. For the first time, we showed the presence of BC particles in human breast milk and found a robust association with ambient air pollution concentrations. Our findings present a pioneering insight into a novel pathway through which combustion-derived air pollution particles can permeate the delicate system of infants.

Increased Risk for Hospitalization Associated with Short Term Particle Air Pollution Exposure for California Residents with Sickle Cell Disease

Background: Air pollution is associated with numerous adverse health outcomes in the general population such as asthma exacerbations, pneumonia, myocardial infarction, and stroke. There is a strong association between air pollution and mechanistic processes associated with oxidative stress, vasoconstriction, and inflammation, suggesting a high risk of exposure for those with sickle cell disease (SCD). A small number of published studies have explored this relationship, primarily focusing on children. Only two studies have examined air pollution in the US, both with outcomes among urban area hospitals in Atlanta, GA. None have taken place in California, which has the worst air quality, including the four most polluted cities in year-round particle pollution in the nation. Additionally, all published studies have overlooked more rural exposure areas. Recent advances in environmental exposure assessment have enabled high-resolution predictions of ambient air pollution concentrations in the whole US, making it possible to cover unmonitored suburban and rural areas that were unrepresented in the past. In addition, these high-resolution predictions are more precise surrogates of the personal exposure measures than monitored data. Particulate matter with a diameter of 2.5 microns or less (PM 2.5) is the most harmful pollutant based on current research findings. We hypothesized that short term increases in PM 2.5 exposure were associated with an increased risk for hospitalization among individuals with SCD in California. Methods: We designed a time-stratified case-crossover study to estimate the percent change in risk for hospitalization for SCD complications associated with each 1mg/m 3 increase in PM 2.5 exposure. The zip code of residence was used to spatially link each hospital admission with PM 2.5 exposure using a novel hybrid exposure model allowing for estimated exposure in areas without direct remote sensing. We used conditional logistic regressions to examine the PM 2.5 exposures at 7-day moving average and single lag days (from lag 0 to 6 days) before admission, adjusting for nonlinear confounding effects of air temperature and humidity at the same exposure window. We examined risk for all hospitalizations and specifically for vaso-occlusive pain (VOC) and acute chest syndrome (ACS). Results: From 2000-2016, among 5,835 California residents with SCD, there were 107,450 admissions for sickle cell disease complications with VOC being the most common indication (71.1%). PM 2.5 air pollution data was examined among 2,634 zip codes throughout the state. Residents with SCD were noted to be living among 1,180 zip codes. The daily burden of PM 2.5 was higher in zip codes with residents with SCD (mean 11.5 mg/m 3, standard deviation [SD] 9.0) when compared to zip codes without residents known to have SCD (mean 9.2 mg/m 3, SD 8.6, p<0.001) ( Table 1). Overall, the mean daily burden of PM 2.5 declined in the period of 2010-2016 when compared to that from 2000-2009. However, the daily mean PM 2.5 concentration remained higher in zip codes with individuals with SCD. No significant association was seen in examining the week-long exposure to PM 2.5 and increase risk in hospitalizations for any indication over the entire study period (2000-2016). However, when examining the more contemporary period of 2010-2016, we identified that each 1mg/m 3 increase in PM 2.5 was associated with 0.22% increased risk for SCD hospitalization (Confidence Interval [CI] 0.02-0.42, p=0.03) 3 days prior to admission. We observed a 0.26% increased risk for hospitalization for VOC with each 1 mg/m 3 increase in PM 2.5 exposure (CI 0.02-0.49, p=0.03) at lag day 3 (Figure 1). No effect was seen with PM 2.5 on hospitalizations for ACS during any time period. Discussion: Our study suggests that short term air pollution exposure may be a trigger for hospitalization for individuals with SCD. In particular this effect is seen in the more recent years with a lower daily average PM 2.5 burden overall, suggesting attention may be needed well below Environmental Protection Agency (EPA) thresholds. Consistent with previous studies, lag day 3 PM 2.5 exposure is associated with an increased risk for hospitalization for VOC. These results may have important implications in understanding risk factors for hospitalization for children and adults with SCD.

Association between short-term air pollution exposure and perturbation in thyrotropin levels in 1.38 million Chinese women: A national longitudinal analysis, 2014–2019

Prevalence of subclinical hypothyroidism substantially increased during the last decade in China, which has been commonly/clinically diagnosed as elevation in thyrotropin (thyroid-stimulating hormone [TSH]). Tobacco smoke containing toxic substances has been linked to thyroid dysfunction; however, data on perturbation of TSH following air pollution exposure in human has not been assessed at nationwide population level. We investigated the longitudinal impact of daily ambient air pollution estimated at residential level on serum TSH in 1.38 million women from China’s 29 mainland provinces between 2014 and 2019. We observed that particulate matter with aerodynamic diameter ≤ 10 and ≤ 2.5 µm (PM10, PM2.5) and nitrogen dioxide (NO2) at cumulative lag 0–7 days of exposure were associated with percent elevations in TSH (0.88% [95% CI: 0.71, 1.05] per [interquartile range, IQR: 54.8 μg/m3] of PM10; 0.89% [95% CI, 0.71, 1.07] per IQR [40.3 μg/m3] of PM2.5; 2.01% [95% CI: 1.81, 2.22] per IQR [27.4 μg/m3] of NO2). Greater associations were observed in participants living in areas with ≥adequate iodine intake and those with low BMI levels and high inflammation status. Our results suggest that increased concentrations of recent ambient air pollutants at exposure ranges commonly encountered in Asia were associated with increases in TSH, supporting disturbing role of short-term air pollution exposure on the regulation of thyroid hormone homeostasis.

The effect of ambient air pollution on birth outcomes in Norway

BackgroundAmbient air pollution can be harmful to the fetus even in countries with relatively low levels of pollution. Most of the established literature estimates the association between air pollution and health rather than causality. In this paper, I examine the causal effects of ambient air pollution on birth outcomes in Norway.MethodsWith the large sample size and geographic division of sub-postal codes in Norway, I can control for a rich set of spatio-temporal fixed effects to overcome most of the endogeneity problems caused by the choice of residential area and date of delivery. After controlling for a rich set of spatio-temporal fixed effects, my paper uses the variance in ambient air pollutant concentrations over narrow time intervals and in a small geographic area of Norway to determine how prenatal air pollution exposure affects birth outcomes. My data contain extensive information about parents as well as meteorological conditions that can be used to control for potential confounding factors.ResultsI find that prenatal exposure to ambient nitric oxide in the last trimester causes significant birth weight and birth length loss under the same sub-postcode fixed effects and calendar month fixed effects, whereas other ambient air pollutants such as nitrogen dioxide and sulfur dioxide appear to be at safe levels for the fetus in Norway. In addition, the marginal adverse effect of ambient nitric oxide is larger for newborns with disadvantaged parents. Both average concentrations of nitric oxide and occasional high concentration events can adversely affect birth outcomes.ConclusionsPrenatal exposure to NO pollution has an adverse effect on birth outcomes. This suggests that government and researchers should pay more attention to examining NO pollution and that health care providers need to advise pregnant women about the risks of air pollution during pregnancy.

Towards healthy and energy-efficient buildings in the context of Egypt: Modelling demand-controlled ventilation to improve the indoor air quality in a generic office space in Cairo

Cairo is characterised by high concentrations of ambient air pollutants, especially air particulate matter of diameter less than 2.5 micrometre (PM2.5). Many studies have emphasized the impact of PM2.5 on people’s health and wellbeing including a World Bank report that has attributed 12% of the total annual deaths in Cairo to the exposure to ambient PM2.5. On one hand. improving the energy efficiency of buildings may involve implementing energy efficiency measures that aim to achieve indoor thermal comfort by maximizing the use of natural ventilation and minimizing mechanical air-conditioning. However, while natural ventilation can help reduce CO2 levels, it can also potentially lead to an increase in indoor PM2.5 levels. This study aims to investigate the impact of multiple air filtration scenarios on the energy consumption and the indoor air quality for a shoebox model that aims to represent generic offices Cairo. The study uses EnergyPlus simulations that leverage an Energy Management System script to model the demand-controlled ventilation, apply air filters when required, and simulate the increase in energy use due to the relevant pressure drops in the air system. The results for the scenarios investigated in the study highlighted that air filters can reduce the average indoor PM2.5 levels by nearly 40% during occupancy hours while causing an estimated increase of around 2-7% in the total operational energy. Given data and assumptions relevant to the study context, it was found that filtering the recirculated air while minimizing the introduction outdoor fresh air can be sufficient to minimize indoor PM2.5 levels.

Adverse Health Impacts of Outdoor Air Pollution, Including from Wildland Fires, in the United States: "Health of the Air," 2018-2020.

Rationale: Adverse health impacts from outdoor air pollution occur across the United States, but the magnitude of these impacts varies widely by geographic region. Ambient pollutant concentrations, emission sources, baseline health conditions, and population sizes and distributions are all important factors that need to be taken into account to quantify local health burdens. Objectives: To determine health impacts from ambient air pollution concentrations in the United States that exceed the levels recommended by the American Thoracic Society. Methods: Using a methodology that has been well established in previous "Health of the Air" reports, this study provides policy-relevant estimates for every monitored county and city in the United States for the adverse health impacts of outdoor pollution concentrations using U.S. Environmental Protection Agency design values for years 2018-2020. Additionally, for the first time, the report includes adverse birth outcomes as well as estimates of health impacts specifically attributable to wildland fires using an exposure dataset generated through Community Multiscale Air Quality simulations. Results: The adverse health burdens attributable to air pollution occur across the entire age spectrum, including adverse birth outcomes (10,660 preterm and/or low-weight births; 95% confidence interval [CI], 3,180-18,330), in addition to mortality impacts (21,300 avoidable deaths; 95% CI, 16,180-26,200), lung cancer incidence (3,000 new cases; 95% CI, 1,550-4,390), multiple types of cardiovascular and respiratory morbidity (748,660 events; 95% CI, 326,050-1,057,080), and adversely impacted days (52.4 million days; 95% CI, 7.9-92.4 million days). Two different estimates of mortality impacts from wildland fires were created based on assumptions regarding the underlying toxicity of particles from wildland fires (low estimate of 4,080 deaths, 95% CI, 240-7,890; middle estimate of 28,000 deaths, 95% CI, 27,300-28,700). Conclusions: This year's report identified sizable health benefits that would be expected to occur across the United States with compliance with more health-protective air quality standards such as those recommended by the American Thoracic Society. This study also indicates that a large number of excess deaths are attributable to emissions from wildland fires; air quality management strategies outside what is required by the Clean Air Act will be needed to best address this important source of air pollution and its associated health risks.

Post-stroke dysphagia and ambient air pollution are associated with dementia.

This cohort study aimed to explore the potential association between ambient air pollution and dementia incidence in adults who have experienced a stroke. Additionally, the study aimed to determine dysphagia as a predictive factor for the subsequent development of dementia in patients with stroke. This retrospective nested case-control study used data from the Kaohsiung Medical University Hospital Database in Taiwan. Data collected include average ambient air pollution concentrations within 3 months and 1 year after the index dysphagia date. The primary outcome includes incident dementia in patients with or without dysphagia. Logistic regression analysis was performed to examine the association between significant air pollution exposure and the risk of dementia while controlling for baseline demographic characteristics (age and sex), and comorbidities. The univariable regression models revealed a higher likelihood of dementia diagnosis in patients with dysphagia (odds ratio = 1.493, 95% confidence interval = 1.000-2.228). The raw odds ratios indicated a potential link between air pollution exposure and elevated dementia risks in the overall study population and patients with stroke without dysphagia, except for O3. Particulate matter (PM)2.5 and nitrogen oxides (NOx) exhibited significant effects on the risk of dementia in the stepwise logistic regression models. The presence of dysphagia following a stroke may pose a risk of developing dementia. Additionally, PM2.5 and NOx exposure appears to elevate the risk of dementia in patients with stroke.

Generating high spatial resolution exposure estimates from sparse regulatory monitoring data

Random Forest algorithms have extensively been used to estimate ambient air pollutant concentrations. However, the accuracy of model-predicted estimates can suffer from extrapolation problems associated with limited measurement data to train the machine learning algorithms. In this study, we developed and evaluated two approaches, incorporating low-cost sensor data, that enhanced the extrapolating ability of random-forest models in areas with sparse monitoring data. Rochester, NY is the area of a pregnancy-cohort study. Daily PM2.5 concentrations from the NAMS/SLAMS sites were obtained and used as the response variable in the model, with satellite data, meteorological, and land-use variables included as predictors. To improve the base random-forest models, we used PM2.5 measurements from a pre-existing low-cost sensors network, and then conducted a two-step backward selection to gradually eliminate variables with potential emission heterogeneity from the base models. We then introduced the regression-enhanced random forest method into the model development. Finally, contemporaneous urinary 1-hydroxypyrene was used to evaluate the PM2.5 predictions generated from the two approaches. The two-step approach increased the average external validation R2 from 0.49 to 0.65, and decreased the RMSE from 3.56 μg/m3 to 2.96 μg/m3. For the regression-enhanced random forest models, the average R2 of the external validation was 0.54, and the RMSE was 3.40 μg/m3. We also observed significant and comparable relationships between urinary 1-hydroxypyrene levels and PM2.5 predictions from both improved models. This PM2.5 model estimation strategy could improve the extrapolating ability of random forest models in areas with sparse monitoring data.

Ambient ozone at a rural Central European site and its vertical concentration gradient close to the ground.

The representativeness of ambient air quality of an in situ measurement is key in the use and correct interpretation of the measured concentration values. Though the horizontal representativeness aspect is generally not neglected in air pollution studies, a detailed, high-resolution vertical distribution of ambient air pollutant concentrations is rarely addressed. The aim of this study is twofold: (i) to explore the vertical distribution of ground-level ozone (O3) concentrations measured at four heights above the ground-namely at 2, 8, 50, and 230m-and (ii) to examine in detail the vertical O3 concentration gradient in air columns between 2 and 8, 8 and 50, and 50 and 230m above the ground. We use the daily mean O3 concentrations measured continuously at the Košetice station, representing the rural Central European background ambient air quality observed during 2015-2021. We use the semiparametric GAM (generalised additive model) approach (with complexity or roughness-penalised splines implementation) to analyse the data with sufficient flexibility. Our models for both O3 concentrations and O3 gradients use (additive) decomposition into annual trend and seasonality (plus an overall intercept). The seasonal and year-to-year patterns of the modelled O3 concentrations look very similar at first glance. Nevertheless, a more detailed look through O3 gradients shows that they differ substantially with respect to their seasonal and long-term dynamics. The vertical O3 concentration gradient in 2-230m is not uniform but changes substantially with increasing height and shows by far the highest dynamics near the ground between 2 and 8m, differing in both the seasonal and annual aspects for all the air columns inspected. We speculate that non-linear changes of both seasonal and annual components of vertical O3 gradients are due to atmospheric-terrestrial interactions and to meteorological factors, which we will explore in a future study.

Association between ambient air pollutants and upper respiratory tract infection and pneumonia disease burden in Thailand from 2000 to 2022: a high frequency ecological analysis

BackgroundA pertinent risk factor of upper respiratory tract infections (URTIs) and pneumonia is the exposure to major ambient air pollutants, with short term exposures to different air pollutants being shown to exacerbate several respiratory conditions.MethodsHere, using disease surveillance data comprising of reported disease case counts at the province level, high frequency ambient air pollutant and climate data in Thailand, we delineated the association between ambient air pollution and URTI/Pneumonia burden in Thailand from 2000 – 2022. We developed mixed-data sampling methods and estimation strategies to account for the high frequency nature of ambient air pollutant concentration data. This was used to evaluate the effects past concentrations of fine particulate matter (PM2.5), sulphur dioxide (SO2), and carbon monoxide (CO) and the number of disease case count, after controlling for the confounding meteorological and disease factors.ResultsAcross provinces, we found that past increases in CO, SO2, and PM2.5 concentration were associated to changes in URTI and pneumonia case counts, but the direction of their association mixed. The contributive burden of past ambient air pollutants on contemporaneous disease burden was also found to be larger than meteorological factors, and comparable to that of disease related factors.ConclusionsBy developing a novel statistical methodology, we prevented subjective variable selection and discretization bias to detect associations, and provided a robust estimate on the effect of ambient air pollutants on URTI and pneumonia burden over a large spatial scale.

Assessing Population Health Risks Posed by Air Pollution Related to Coal Mine Reclamation

Introduction: There is an urgent need for reclamation of abandoned coal mine lands in Kuzbass to reduce their adverse effects on the environment. Objective: To assess risks to public health posed by air pollution associated with coal mine liquidation and land reclamation. Materials and methods: We used data on the volume of maximum permissible emissions of the abandoned coal mine area for 2019 to assess the spread and exposure to pollutants at 40 points selected on the map of the city of Prokopyevsk, Kemerovo Region, and to estimate the maximum and mean concentrations of the contaminants. The values of carcinogenic risk and risks of non-carcinogenic effects were calculated in compliance with the Russian Guidelines R 2.1.10.1920–04, Human Health Risk Assessment from Environmental Chemicals, while risks of chronic toxicity were assessed using methods by Shcherbo et al. The calculated risk values were then compared with their acceptable levels. Risks were also assessed with account for the contribution of background concentrations of chemicals. Results: The list of priority pollutants included inorganic dust containing 20–70 % SiO2, nitrogen dioxide, nitrogen oxide, sulfur dioxide, carbon (soot), carbon monoxide, and inorganic dust containing < 20 % SiO2. The estimated maximum and mean concentrations of airborne pollutants during mine reclamation activities were not exceeded at any exposure point. Hazard indices for acute exposures to pollutants exceeded the acceptable level at three points located closer to the sources. Carcinogenic risks did not exceed the level of acceptable risk at any point. Total risks of chronic toxicity were 1.2 and 1.75 times higher than acceptable at two points. Hazard indices estimated with account for the background levels of pollutants were 1.448–2.603 times higher than the acceptable level at all exposure points. The carcinogenic risk assessed taking into account the background levels was found to be acceptable. The most affected (target) critical organs and systems were respiratory organs and blood. Total risks of chronic poisoning assessed with account for background concentrations of ambient air pollutants exceeded the acceptable level by 3.15–5.40 times at all points. Conclusions: Reclamation activities have no significant effect on ambient air quality and health of the population of Prokopyevsk. Total risks of chronic toxicity and hazard indices exceed acceptable levels in residential areas located close to the reclamation site. High hazard indices and total risks of chronic toxicity established at all exposure points taking into account background concentrations of contaminants, are determined by generally poor environmental conditions in the city with the intensively developing coal industry.

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions

There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.

Association between air pollutants and initiation of biological therapy in patients with ankylosing spondylitis: a nationwide, population-based, nested case–control study

BackgroundOutdoor air pollution has been found to trigger systemic inflammatory responses and aggravate the activity of certain rheumatic diseases. However, few studies have explored the influence of air pollution on the activity of ankylosing spondylitis (AS). As patients with active AS in Taiwan can be reimbursed through the National Health Insurance programme for biological therapy, we investigated the association between air pollutants and the initiation of reimbursed biologics for active AS.MethodsSince 2011, hourly concentrations of ambient air pollutants, including PM2.5, PM10, NO2, CO, SO2, and O3, have been estimated in Taiwan. Using Taiwanese National Health Insurance Research Database, we identified patients with newly diagnosed AS from 2003 to 2013. We selected 584 patients initiating biologics from 2012 to 2013 and 2336 gender-, age at biologic initiation-, year of AS diagnosis- and disease duration-matched controls. We examined the associations of biologics initiation with air pollutants exposure within 1 year prior to biologic use whilst adjusting for potential confounders, including disease duration, urbanisation level, monthly income, Charlson comorbidity index (CCI), uveitis, psoriasis and the use of medications for AS. Results are shown as adjusted odds ratio (aOR) with 95% confidence intervals (CIs).ResultsThe initiation of biologics was associated with exposure to CO (per 1 ppm) (aOR, 8.57; 95% CI, 2.02–36.32) and NO2 (per 10 ppb) (aOR, 0.23; 95% CI, 0.11–0.50). Other independent predictors included disease duration (incremental year, aOR, 8.95), CCI (aOR, 1.31), psoriasis (aOR, 25.19), use of non-steroidal anti-inflammatory drugs (aOR, 23.66), methotrexate use (aOR, 4.50; 95% CI, 2.93–7.00), sulfasalazine use (aOR, 12.16; 95% CI, 8.98–15.45) and prednisolone equivalent dosages (mg/day, aOR, 1.12).ConclusionsThis nationwide, population-based study revealed the initiation of reimbursed biologics was positively associated with CO levels, but negatively associated with NO2 levels. Major limitations included lack of information on individual smoking status and multicollinearity amongst air pollutants.

Impact of the COVID-19 lockdown on typical ambient air pollutants: Cyclical response to anthropogenic emission reduction

Preliminary studies have confirmed that ambient air pollutant concentrations are significantly influenced by the COVID-19 lockdown measures, but little attention focus on the long term impacts of human countermeasures in cities all over the world during the period. Still, fewer have addressed their other essential properties, especially the cyclical response to concentration reduction. This paper aims to fill the gaps with combined methods of abrupt change test and wavelet analysis, research areas were made of five cities, Wuhan, Changchun, Shanghai, Shenzhen and Chengdu, in China. Abrupt changes in contaminant concentrations commonly occurred in the year prior to the outbreak. The lockdown has almost no effect on the short cycle below 30 d (days) for both pollutants, and a negligible impact on the cycle above 30 d. PM2.5 (fine particulate matter) has a stable short-cycle nature, which is greatly influenced by anthropogenic emissions. The analysis revealed that the sensitivity of PM2.5 to climate is increased along with the concentrations of PM2.5 were decreasing by the times when above the threshold (30–50 μg m−3), and which could lead to PM2.5 advancement relative to the ozone phase over a period of 60 d after the epidemic. These results suggest that the epidemic may have had an impact earlier than when it was known. And significant reductions in anthropogenic emissions have little impact on the cyclic nature of pollutants, but may alter the inter-pollutant phase differences during the study period.

Impacts of COVID-19's restriction measures on personal exposure to VOCs and aldehydes in Taipei City

The Coronavirus Disease 2019 (COVID-19) pandemic provided an unprecedented natural experiment, that allowed us to investigate the impacts of different restrictive measures on personal exposure to specific volatile organic compounds (VOCs) and aldehydes and resulting health risks in the city. Ambient concentrations of the criteria air pollutants were also evaluated. Passive sampling for VOCs and aldehydes was conducted for graduate students and ambient air in Taipei, Taiwan, during the Level 3 warning (strict control measures) and Level 2 alert (loosened control measures) of the COVID-19 pandemic in 2021–2022. Information on the daily activities of participants and on-road vehicle counts nearby the stationary sampling site during the sampling campaigns were recorded. Generalized estimating equations (GEE) with adjusted meteorological and seasonal variables were used to estimate the effects of control measures on average personal exposures to the selected air pollutants. Our results showed that ambient CO and NO2 concentrations in relation to on-road transportation emissions were significantly reduced, which led to an increase in ambient O3 concentrations. Exposure to specific VOCs (benzene, methyl tert-butyl ether (MTBE), xylene, ethylbenzene, and 1,3-butadiene) associated with automobile emissions were remarkably decreased by ~40–80 % during the Level 3 warning, resulting in 42 % and 50 % reductions of total incremental lifetime cancer risk (ILCR) and hazard index (HI), respectively, compared with the Level 2 alert. In contrast, the exposure concentration and calculated health risks in the selected population for formaldehyde increased by ~25 % on average during the Level 3 warning. Our study improves knowledge of the influence of a series of anti-COVID-19 measures on personal exposure to specific VOCs and aldehydes and its mitigations.

Association between annual exposure to air pollution and systolic blood pressure among adolescents in Montréal, Canada

In adults, chronic exposure to air pollution is associated with elevated blood pressure, but few studies have examined this relationship in youth. We investigated the association between annual ambient concentrations of air pollutants (fine particulate matter [PM2.5] and nitrogen dioxide [NO2]) and systolic blood pressure (SBP) among adolescents in Montréal, Canada. Participants were students aged 15 to 17 years who provided SBP and residential postal code data in 2004/05 through their enrolment in the Nicotine Dependence in Teens study. Annual estimates for 2004 of residential exposure to NO2 and PM2.5 were provided by the Canadian Urban Environmental Health Research Consortium and linked to participants' residential postal code. Elevated SBP was defined as SBP ≥ 90th percentile adjusted for age, sex and height. Logistic regression was used to estimate odds ratios and 95% confidence intervals (CIs) for each pollutant with respect to elevated SBP, adjusted for relevant confounders. The sample consisted of 508 adolescents (mean age: 16.9, 46% male); 4% had elevated SBP. Although estimates were not statistically significant, there were generally modest positive associations between pollutant levels and SBP. The adjusted prevalence odds ratio of elevated SBP was 1.33 (95% CI: 0.64, 3.05) for every interquartile range (IQR) increase in residential PM2.5 levels (2.1μg/m3). Similarly, the adjusted prevalence odds ratio of elevated SBP was 1.17 (95% CI: 0.47, 2.70) for every IQR increase in residential NO2 levels (10.2 ppb). Findings support a possible relationship between exposure to air pollutants and increased SBP in adolescents, warranting further investigation for this important public health concern.

Unexpected association between ambient ozone and adult insomnia outpatient visits: A large-scale hospital-based study

Growing evidence indicates that short-term ozone (O3) exposure has substantial health consequences, but the relationship between short-term ambient O3 and insomnia, a common sleep disorder, is not clear. This study aimed to investigate the short-term effects of ambient O3 exposure on outpatient visits for adult insomnia and to explore the potential modifiers. A large-scale multihospital-based study was carried out in Chongqing, the largest city in Southwest China. Daily data on outpatient visits for adult insomnia, average concentrations of ambient air pollutants and meteorological factors were collected. We conducted quasi-Poisson regression with generalized additive model to assess the association between ambient O3 and outpatient visits for adult insomnia in varied windows of exposure. Subgroup analyses were applied to identify its modifiers. Totally, 140,159 adult insomnia outpatient visits were identified. The daily maximum 8-h average concentration of O3 was 69 μg/m3 during the study period, which greatly below the updated Chinese and WHO recommended limits (daily maximum 8-h average, O3: 100 μg/m3). Short-term O3 exposure was significantly negatively associated with outpatient visits for adult insomnia in different lag periods and the greatest decrease of outpatient visits for adult insomnia was found at lag 02 [0.93% (95% CI: 0.48%, 1.38%)]. Additionally, stronger links between O3 and adult insomnia outpatient visits were presented in cool seasons, and we did not observe any significant modified effects of gender and age. Moreover, the negative O3-insomnia association remained robust after controlling for other common air pollutants and comorbidities. In summary, short-term exposure to lower level of ambient O3, was associated with reduced daily outpatient visits for adult insomnia and such association showed to be more obvious in cool seasons.