Traffic-related Sources Research Articles

Coupling MATSim and the PALM Model System—Large Scale Traffic and Emission Modeling with High-Resolution Computational Fluid Dynamics Dispersion Modeling

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NOx concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas.

Long-term contributions of VOC sources and their link to ozone pollution in Bronx, New York City

Changes in energy and environmental policies along with changes in the energy markets of New York State over the past two decades, have spurred interest in evaluating their impacts on emissions from various energy generation sectors. This study focused on quantifying these effects on VOC (volatile organic compounds) emissions and their subsequent impacts on air quality within the New York City (NYC) metropolitan area. NYC is an EPA nonattainment region for ozone (O3) and likely is a VOC limited region. NYC has a complex coastal topography and meteorology with low-level jets and sea/bay/land breeze circulation associated with heat waves, leading to summertime O3 exceedances and formation of secondary organic aerosol (SOA). To date, no comprehensive source apportionment studies have been done to understand the contributions of local and long-range sources of VOCs in this area. This study applied an improved Positive Matrix Factorization (PMF) methodology designed to incorporate atmospheric dispersion and photochemical reaction losses of VOCs to provide improved apportionment results. Hourly measurements of VOCs were obtained from a Photochemical Assessment Monitoring Station located at an urban site in the Bronx from 2000 to 2021. The study further explores the role of VOC sources in O3 and SOA formation and leverages advanced machine learning tools, XGBoost and SHAP algorithms, to identify synergistic interactions between sources and provided VOC source impacts on ambient O3 concentrations. Isoprene demonstrated a substantial influence in the source contribution of the biogenic factor, emphasizing its role in O3 formation. Notable contributions from anthropogenic emissions, such as fuel evaporation and various industrial processes, along with significant traffic-related sources that likely contribute to SOA formation, underscore the combined impact of natural and human-made sources on O3 pollution. Findings of this study can assist regulatory agencies in developing appropriate policy and management initiatives to control O3 pollution in NYC.

Source-Specific Air Pollution and Loss of Independence in Older Adults Across the US

Air pollution is a recognized risk factor associated with chronic diseases, including respiratory and cardiovascular conditions, which can lead to physical and cognitive impairments in later life. Although these losses of function, individually or in combination, reduce individuals' likelihood of living independently, little is known about the association of air pollution with this critical outcome. To investigate associations between air pollution and loss of independence in later life. This cohort study was conducted as part of the Environmental Predictors Of Cognitive Health and Aging study and used 1998 to 2016 data from the Health and Retirement Study. Participants included respondents from this nationally representative, population-based cohort who were older than 50 years and had not previously reported a loss of independence. Analyses were performed from August 31 to October 15, 2023. Mean 10-year pollutant concentrations (particulate matter less than 2.5 μm in diameter [PM2.5] or ranging from 2.5 μm to 10 μm in diameter [PM10-2.5], nitrogen dioxide [NO2], and ozone [O3]) were estimated at respondent addresses using spatiotemporal models along with PM2.5 levels from 9 emission sources. Loss of independence was defined as newly receiving care for at least 1 activity of daily living or instrumental activity of daily living due to health and memory problems or moving to a nursing home. Associations were estimated with generalized estimating equation regression adjusting for potential confounders. Among 25 314 respondents older than 50 years (mean [SD] baseline age, 61.1 [9.4] years; 11 208 male [44.3%]), 9985 individuals (39.4%) experienced lost independence during a mean (SD) follow-up of 10.2 (5.5) years. Higher exposure levels of mean concentration were associated with increased risks of lost independence for total PM2.5 levels (risk ratio [RR] per 1-IQR of 10-year mean, 1.05; 95% CI, 1.01-1.10), PM2.5 levels from road traffic (RR per 1-IQR of 10-year mean, 1.09; 95% CI, 1.03-1.16) and nonroad traffic (RR per 1-IQR of 10-year mean, 1.13; 95% CI, 1.03-1.24), and NO2 levels (RR per 1-IQR of 10-year mean, 1.05; 95% CI, 1.01-1.08). Compared with other sources, traffic-generated pollutants were most consistently and robustly associated with loss of independence; only road traffic-related PM2.5 levels remained associated with increased risk after adjustment for PM2.5 from other sources (RR per 1-IQR increase in 10-year mean concentration, 1.10; 95% CI, 1.00-1.21). Other pollutant-outcome associations were null, except for O3 levels, which were associated with lower risks of lost independence (RR per 1-IQR increase in 10-year mean concentration, 0.94; 95% CI, 0.92-0.97). This study found that long-term exposure to air pollution was associated with the need for help for lost independence in later life, with especially large and consistent increases in risk for pollution generated by traffic-related sources. These findings suggest that controlling air pollution could be associated with diversion or delay of the need for care and prolonged ability to live independently.

Road Traffic Emissions Lead to Much Enhanced New Particle Formation through Increased Growth Rates.

New particle formation (NPF) is a major source of atmospheric aerosol particles, including cloud condensation nuclei (CCN), by number globally. Previous research has highlighted that NPF is less frequent but more intense at roadsides compared to urban background. Here, we closely examine NPF at both background and roadside sites in urban Central Europe. We show that the concentration of oxygenated organic molecules (OOMs) is greater at the roadside, and the condensation of OOMs along with sulfuric acid onto new particles is sufficient to explain the growth at both sites. We identify a hitherto unreported traffic-related OOM source contributing 29% and 16% to total OOMs at the roadside and background, respectively. Critically, this hitherto undiscovered OOM source is an essential component of urban NPF. Without their contribution to growth rates and the subsequent enhancements to particle survival, the number of >50 nm particles produced by NPF would be reduced by a factor of 21 at the roadside site. Reductions to hydrocarbon emissions from road traffic may thereby reduce particle numbers and CCN counts.

Identification of solid phase speciation of trace elements in dust dry deposition using focused ion beam and selected area electron diffraction

BackgroundFew studies have been conducted to identify solid phase speciation of trace elements within the interior of airborne particles, although the adverse effects of ambient particles are closely related to the speciation of toxic elements and therefore the identification of chemical binding sites and solid phase associations is essential for assessing the impacts of trace elements in ambient air on environmental quality and human health. ObjectiveA combined use of focused ion beam scanning electron microscopy (FIB-SEM) and selected area electron diffraction (SAED) patterns from transmission electron microscopy (TEM) was applied with an energy dispersive X-ray spectroscopy (EDS) to investigate the solid phase speciation of trace elements within the interior of dust dry deposition obtained from an urban area. ResultsThe study results using the high-resolution microscopic techniques for the characterization of nano- and micron-sized particles revealed lead chromate for Pb and Cr6+, barite for Ba, rutile for Ti, and calcite, gypsum, and quicklime for Ca within the carbon matrix, implying their traffic-related sources. Fe oxides were observed for Fe, Mn, Ni, and Cr3+, with cerianite and Fe-La alloys for rare earth elements (REEs). ConclusionsThree types of anthropogenic sources were inferred based on the morphology and texture of the solid phases, as well as their primary usage: traffic road marking paints for Pb, Cr6+, Ba, and Ti, cement materials for Ca, and industrial activities for Fe, Mn, Ni, Cr3+, and REEs. The presence of submicron-sized toxic metal-bearing particles in dust dry deposition indicated that the toxic metals (i.e., Pb, Cr6+) can penetrate into the respiratory system along with the nanoparticles. The study results demonstrate that the solid phase speciation of trace elements detected using FIB-SEM and TEM-SAED can be effectively utilized for the identification of sources and the assessment of chemical toxicity of atmospheric dust.

Seasonal variability of volatile organic compounds (VOCs) at a high-altitude station in the Western Ghats, India: Influence of biogenic, anthropogenic emissions and long-range transport

Seasonal variability of volatile organic compounds (VOCs) was studied using year-long observations (June 2019–May 2020) over a high-altitude (1380m AMSL) forested site in the Western Ghats of India. Isoprene, a well-known biogenic VOC, peaked during pre-monsoon. Temperature stress and higher photo synthetically active radiation (PAR) were the drivers behind isoprene emissions from the surrounding forested regions. The daytime average isoprene mixing ratio was 1.92 ± 1.51 ppb during summer and the night time average was 0.02 ± 0.01 ppb. A strong relationship between isoprene mixing ratio and ambient temperature was derived. Most of the anthropogenic VOCs like acetonitrile, benzene, toluene and xylene were found to be higher during winter. These anthropogenic VOCs mostly exhibited afternoon to evening peaks, which suggests the contribution by traffic emissions along with other combustion sources (biofuels) and vertical updraft of pollutants from the surrounding valley region. The toluene to benzene ratio (T/B) was >1 during the afternoon and evening hours in monsoon, whereas, aged air masses with lower T/B ratios (<0.7) prevailed during other seasons. Polar bi-variate plots reveal that toluene, xylene and tri-methyl benzene (TMB) were mostly emitted at the south-west of the measurement site, irrespective of the season, indicating a dominant local traffic related source throughout the year. The contribution of local emissions and regional background on two selected aromatic VOCs, toluene and xylene was estimated, revealing the dominance of local anthropogenic sources during monsoon and post monsoon. Positive matrix factorization revealed three source factors in all the seasons with one additional factor during winter and pre-monsoon. The first factor was constituted of toluene, benzene, xylene and TMB, and the T/B ratio was ∼1.2, indicating traffic/fossil fuel contribution. A second ‘biomass burning’ factor was dominated by acetonitrile, ethanal, acetone, and benzene in which the T/B ratio was 0.45. Factors one & two were further validated by comparison with chamber experiments conducted using different combustion sources and solvents. The third factor was dominated by isoprene and monoterpenes which were attributed to biogenic emissions. The fourth factor comprised mainly of acetonitrile, ethanal, and acetone indicating aged air masses due to long-range transport with contributions from photochemical production and biomass burning.

Reducing Tyre Wear Emissions of Automated Articulated Vehicles through Trajectory Planning.

Effective emission control technologies and eco-friendly propulsion systems have been developed to decrease exhaust particle emissions. However, more work must be conducted on non-exhaust traffic-related sources such as tyre wear. The advent of automated vehicles (AVs) enables researchers and automotive manufacturers to consider ways to further decrease tyre wear, as vehicles will be controlled by the system rather than by the driver. In this direction, this work presents the formulation of an optimal control problem for the trajectory optimisation of automated articulated vehicles for tyre wear minimisation. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one to minimise tyre wear in fixed time cases. Specific boundaries and constraints are applied to the problem to ensure the vehicle's stability and the feasibility of the solution. According to the results, a small increase in the journey time leads to a significant decrease in the mass loss due to tyre wear. The employment of articulated vehicles with low powertrain capabilities leads to greater tyre wear, while excessive increases in powertrain capabilities are not required. The conclusions pave the way for AV researchers and manufacturers to consider tyre wear in their control modules and come closer to the zero-emission goal.

Contributions and limitations of environmental magnetism to characterize traffic-related particulate matter sources

SUMMARY The chronic exposure to particulate matter (PM) pollution causes societal and environmental issues, in particular in urban areas where most citizen are regularly exposed to vehicular traffic. Since almost two decades, environmental magnetic monitoring has demonstrated its efficiency to successfully map relative concentrations of airborne particle deposition on accumulative surfaces. A better understanding of the magnetic results requires discriminating the main traffic-related sources of the observed signal on particle collectors. To meet this objective, we investigated a sample set of exhaust and non-exhaust sources with respect to their magnetic fingerprints inferred from hysteresis loops, first-order reversal curve (FORC) diagrams, temperature dependency of initial susceptibility and unmixing of isothermal remanent magnetization acquisition curves. The source sample set comprises 14 diesel and gasoline exhaust smoke residues, 12 abrasive-fatigue wear test pieces from worn brake-pads, brake powders, worn tire-tread and three resuspension products: asphalt concrete, street dust and Saharan mineral dust deposited by precipitation after long-range eolian transport. Magnetic properties of the source samples were compared to those from various accumulative surfaces exposed to urban traffic (passive collectors, filters of facemasks for cycling, plant leaves and tree barks). We found some fingerprints of exhaust pipes and brake wear products on these collectors. The findings highlight the relevance of environmental magnetism tools to characterize different traffic-related source signals in accumulative surfaces in urban environment.

Influence of particles formed resulting from wear of automobile brake mechanisms on human health

In this article, details of the brake mechanism and the method of determining the concentration of metals in the particles formed from the wear of tires as a result of the movement of cars, as well as a comparative analysis of road dust formed on asphalt-concrete roads of different composition are covered. In cities’ traffic-related sources are a substantial source of particulate pollution. The following categories of traffic-related particles exist: brake mechanism details, formed as a result of tire wear and wear to the road surface, accumulate due to turbulence caused by traffic, and exhaust particles released as a result of incomplete combustion of fuel and volatilization of lubricants during combustion. The method of determining the metal concentration in the formed particles, data on the physical and chemical properties of the particles are presented. Information is given on the negative impact on human health of the particles formed as a result of wear of the details of the car brake mechanism.

Analysis of the quantity of particles emitted to the environment as a result of the brake linings wear of specialized transport vehicles

This article examines the composition of particles emitted into the environment as a result of brake lining degradation during the braking of specialized cars. The amount of particles emitted into the environment has been theoretically studied, taking into account that the resource duration of brake linings on specialized vehicles varies based on the adhesion coefficient of wheel contact with the road. The distribution of metal concentrations in particles created by the brake linings wear during the braking of specialized vehicles is extensively studied. Traffic-related sources contribute significantly to particulate matter in cities. It was discovered that the samples collected during the braking of specialized vehicles contain high levels of metal elements such as iron, copper, zinc, chromium, tin and antimony. Currently, there is information about the negative impact of the particles formed during braking of cars on the environment.

Traffic-related air pollution and Parkinson's disease in central California

BackgroundPrior studies suggested that air pollution exposure may increase the risk of Parkinson's Disease (PD). We investigated the long-term impacts of traffic-related and multiple sources of particulate air pollution on PD in central California. MethodsOur case-control analysis included 761 PD patients and 910 population controls. We assessed exposure at residential and occupational locations from 1981 to 2016, estimating annual average carbon monoxide (CO) concentrations - a traffic pollution marker - based on the California Line Source Dispersion Model, version 4. Additionally, particulate matter (PM2.5) concentrations were based on a nationwide geospatial chemical transport model. Exposures were assessed as 10-year averages with a 5-year lag time prior to a PD diagnosis for cases and an interview date for controls, subsequently categorized into tertiles. Logistic regression models were used, adjusting for various factors. ResultsTraffic-related CO was associated with an increased odds ratio for PD at residences (OR for T3 vs. T1: 1.58; 95% CI: 1.20, 2.10; p-trend = 0.02) and workplaces (OR for T3 vs. T1: 1.91; 95% CI: 1.22, 3.00; p-trend <0.01). PM2.5 was also positively associated with PD at residences (OR for T3 vs. T1: 1.62; 95% CI: 1.22, 2.15; p-trend <0.01) and workplaces (OR for T3 vs. T1: 1.85; 95% CI: 1.21, 2.85; p-trend <0.01). Associations remained robust after additional adjustments for smoking status and pesticide exposure and were consistent across different exposure periods. ConclusionWe found that long-term modeled exposure to local traffic-related air pollution (CO) and fine particulates from multiple sources (PM2.5) at homes and workplaces in central California was associated with an increased risk of PD.

The SPASS dataset: A new synthetic polyphonic dataset with spatiotemporal labels of sound sources

Environmental noise in urban settings has several adverse effects on the population’s health and quality of life. Detecting and classifying sound sources is a primary task to control this noise. Currently, advanced sound event detection (SED) is achieved through the use of deep learning (DL) models. However, one downside of DL is the large amount of labeled data required to train the models effectively. Labeling large datasets is costly and time-consuming, and synthetically generated datasets created so far have yet to be able to provide the spatiotemporality of the sound sources within the soundscape. To address these problems, this paper introduces a synthetic polyphonic ambient sound source (SPASS), a novel synthetic polyphonic dataset that features spatiotemporal labels of sound sources. The new dataset, created with acoustic virtual reality tools, is used to pre-train a DL model for polyphonic SED in urban soundscapes. The SPASS dataset was created by simulating five different soundscapes to ensure that DL models are not tailored to any specific soundscape type. The DL model was evaluated using a transfer learn- ing methodology on two real-world SED tasks involving urban sound events. The DL model pre-trained with SPASS significantly outperformed that trained solely on actual data and achieved similar results as one pre-trained using Google Au- dioset, a dataset that is 100 times larger than SPASS. Additionally, pre-training with the new SPASS dataset proved to be better at effectively detecting and classifying traffic-related sound sources in urban soundscapes.

Efficacy of green infrastructure in reducing exposure to local, traffic-related sources of airborne particulate matter (PM)

One aim of roadside green infrastructure (GI) is to mitigate exposure to local, traffic-generated pollutants. Here, we determine the efficacy of roadside GI in improving local air quality through the deposition and/or dispersion of airborne particulate matter (PM). PM was collected on both pumped air filters and on the leaves of a recently installed ‘tredge’ (trees managed as a head-high hedge) at an open road environment next to a primary school in Manchester, U.K. The magnetic properties of PM deposited on leaves and filters (size fractions PM10 and PM2.5) were deduced from hysteresis loops, first-order reversal curves (FORCs), and low-temperature remanence measurements. These were complemented with electron microscopy to identify changes in magnetic PM concentration downwind of the tredge/GI. We show that the tredge is permeable to airflow using a simple CO2 tracer experiment; hence, it allows interception and subsequent deposition of PM on its leaves. Magnetic loadings per m3 of air from filters (PM10 saturation magnetisation, Ms, at 5 K) were reduced by 40 % behind the tredge and a further 63 % in the playground; a total reduction of 78 % compared to roadside air. For the PM2.5 fraction, the reduction in magnetic loading behind the tredge was remarkable (82 %), reflecting efficient diffusional capture of sub-5 nm Fe-oxide particles by the tredge. Some direct mixing of roadside and playground air occurs at the back of the playground, caused by air flow over, and/or through gaps in, the slowly-permeable tredge. The magnetic loading on tredge leaves increased over successive days, capturing ~23 % of local, traffic-derived PM10. Using a heuristic two-dimensional turbulent mixing model, we assess the limited dispersion of PM < 22.5 μm induced by eddies in the tredge wake. This study demonstrates that PM deposition on leaves reduces exposure significantly in this school playground setting; hence, providing a cost-effective mitigation strategy.

Methods of determining the amount of harmful particles released into the environment as a result of car tire wear

This article describes determining methods of metal emissions in the composition of small particles coming by brake mechanisms details and tire wear as a result of vehicle movement, and dust to be formed by comparative analysis on asphalt-concrete roads of different compositions. A significant contributor to particulate matter in cities is traffic-related sources. Traffic-related particles can be divided into exhaust particles released by the incomplete combustion of fuel and the volatilization of lubricants during combustion and formed by brake mechanism parts, tire wear and road surface wear, and accumulated due to turbulence caused by road traffic. The method of determining the metal concentration in the formed particles gives information on the physico-chemical properties of the particles.

Temporal Variations and Spatial Distribution of Air Pollutants in Shaoxing, a City in Yangtze Delta, China Based on Mobile Monitoring Using a Sensor Package

Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are easily exposed to TRAPs in their daily lives. However, long-term exposure to TRAPs can have adverse health effects. Mobile monitoring is more flexible compared to traditional urban monitoring stations and can effectively obtain the spatial variation characteristics of air pollutants. We mounted a sensor package on an electric bicycle and conducted mobile measurements of CO, NO2 and SO2 on a circular road in the center of Shaoxing, a city in the center of the Yangtze Delta, China. The CO, NO2 and SO2 concentrations were observed to be higher in the morning and evening rush hours, and the three pollutants show different seasonal and spatial variation characteristics. CO concentration was higher in urban arterial and crossroads. NO2 concentration was variable, alternating between high and low concentrations. SO2 concentration was relatively stable and aggregated. This study provides important information on the spatial and temporal variations of TRAPs, which helps commuters understand how to effectively reduce pollutant exposure during personal travel.

Emission profiles, source identifications, and health risk of polycyclic aromatic hydrocarbons (PAHs) in a road tunnel located in Xi'an, China.

Understanding the sources and characteristics of PM2.5-bound PAHs from traffic-related pollution can provide valuable data for mitigating air contamination from traffic in local urban regions. However, little information on PAHs is available regarding the typical arterial highway-Qinling Mountains No.1 tunnel in Xi'an. We estimated the profiles, sources, and emission factors of PM2.5-bound PAHs in this tunnel. The total PAH concentrations were 22.78 ng·m-3 and 52.80 ng·m-3 at the tunnel middle and exit, which were 1.09 and 3.84 times higher than that at the tunnel entrance. Pyr, Flt, Phe, Chr, BaP, and BbF were the dominant PAH species (representing approximately 78.01% of total PAHs). The four rings PAHs were dominant (58%) among the total PAH concentrations in PM2.5. The results demonstrated that diesel and gasoline vehicles exhaust emissions contributed 56.81% and 22.60% to the PAHs, respectively, while the corresponding value for together brakes, tyre wear, and road dust was 20.59%. The emission factors of total PAHs were 29.35 μg·veh-1·km-1, and emission factors of 4 rings PAHs were significantly higher than those of the other PAHs. The sum of ILCR was estimated to be 1.41×10-4, which accorded with acceptable level of cancer risk (10-6-10-4), PAHs should not ignored as they still affect the public health of inhabitants. This study shed light on PAH profiles and traffic-related sources in the tunnel, thereby facilitating the assessment of control measures targeting PAHs in local areas.

Modeling atmospheric microplastic cycle by GEOS-Chem: An optimized estimation by a global dataset suggests likely 50 times lower ocean emissions

Modeling atmospheric microplastic cycle by GEOS-Chem: An optimized estimation by a global dataset suggests likely 50 times lower ocean emissions

A supervised machine learning approach to classify traffic-derived PM sources based on their magnetic properties.

Environmental magnetism techniques are increasingly used to map the deposition of particulate pollutants on any type of accumulative surfaces. The present study is part of a collective effort that begun in recent years to evaluate the efficiency of these techniques involving a large range of measurements to trace the source signals. Here we explore the possibilities provided by the very simple but robust k-near-neighbors algorithm (kNN) for classification in a source-to-sink approach. For this purpose, in a first phase, the magnetic properties of the traffic-related sources of particulate matter (tire, brake pads, exhaust pipes, etc.) are used to parameterize and train the model. Then, the magnetic parameters measured on accumulating surfaces exposed to a polluted air as urban plant leaves and passive filters are confronted to the model. The results are very encouraging. The algorithm predicts the dominant traffic-related sources for different kinds of accumulative surfaces. The model predictions are generally consistent according to the sampling locations. Its resolution seems adequate since different dominant sources could be identified within one street. We demonstrate the possibility to trace traffic-derived pollutants from sources to sinks based only on magnetic properties, and to eventually quantify their contributions in the total magnetic signal measured. Because magnetic mapping has a high-resolution efficiency, these results open the opportunity to complement conventional methods used to measure air quality and to improve the numerical models of pollutant dispersion.

The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9–10 μg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 μg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and PM2.5 levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021.

Associations between source-apportioned PM2.5 and 30-day readmissions in heart failure patients

BackgroundAir pollution exposure is a significant risk factor for morbidity and mortality, especially for those with pre-existing chronic disease. Previous studies highlighted the risks that long-term particulate matter exposure has for readmissions. However, few studies have evaluated source and component specific associations particularly among vulnerable patient populations. ObjectivesUse electronic health records from 5556 heart failure (HF) patients diagnosed between July 5, 2004 and December 31, 2010 that were part of the EPA CARES resource in conjunction with modeled source-specific fine particulate matter (PM2.5) to estimate the association between exposure to source and component apportioned PM2.5 at the time of HF diagnosis and 30-day readmissions. MethodsWe used zero-inflated mixed effects Poisson models with a random intercept for zip code to model associations while adjusting for age at diagnosis, year of diagnosis, race, sex, smoking status, and neighborhood socioeconomic status. We undertook several sensitivity analyses to explore the impact of geocoding precision and other factors on associations and expressed associations per interquartile range increase in exposures. ResultsWe observed associations between 30-day readmissions and an interquartile range increase in gasoline- (16.9% increase; 95% confidence interval = 4.8%, 30.4%) and diesel-derived PM2.5 (9.9% increase; 95% confidence interval = 1.7%, 18.7%), and the secondary organic carbon component of PM2.5 (SOC; 20.4% increase; 95% confidence interval = 8.3%, 33.9%). Associations were stable in sensitivity analyses, and most consistently observed among Black study participants, those in lower income areas, and those diagnosed with HF at an earlier age. Concentration-response curves indicated a linear association for diesel and SOC. While there was some non-linearity in the gasoline concentration-response curve, only the linear component was associated with 30-day readmissions. DiscussionThere appear to be source specific associations between PM2.5 and 30-day readmissions particularly for traffic-related sources, potentially indicating unique toxicity of some sources for readmission risks that should be further explored.