The health impacts of weekday traffic: A health risk assessment of PM2.5 emissions during congested periods

Road congestion and air pollution -Analysis of spatial and temporal congestion effects

Urban traffic congestion has resulted in several adverse outcomes, including reduced traffic efficiency, increased noise pollution, and heightened exhaust emissions. It has also emerged as a significant indicator of urban health concerns. This article primarily delves into an examination of the pollution stemming from congestion. To accomplish this, the study focuses on two specific aspects of congestion measurement: long-term spatial constraints (limited travel routes) and short-term time delays (time wasted due to congestion). Expanding on this, the article explores the potential solutions to mitigate pollution effects through measures such as optimizing space utilization through public transportation systems like subways and strategically scheduling travel during holidays. These considerations are incorporated within the article's scope. Additionally, in order to address endogeneity concerns, the research conducts instrumental variable effectiveness tests from both temporal and spatial perspectives. The outcomes highlight the degradation of air quality and the increase in total traffic congestion in both the long and short term, while also indicating the presence of genuine methods to alleviate these issues. Consequently, effective collaborative efforts for prevention and control are imperative to combat environmental and traffic pollution. Moreover, optimizing sustainable urban development plans to enhance land utilization plays a pivotal role in minimizing the external costs associated with long-distance commuting.

Roadway construction as a natural experiment to examine air pollution impacts on infant health

Traffic-related air pollution (TRAP) poses a significant public health risk that is associated with adverse birth outcomes. Large roadway infrastructure projects present a natural experiment to examine how resulting congestion change is associated with adverse birth outcomes for nearby populations. This study is designed to examine the influence of living close to a roadway before, during, and after a construction project using a difference-in-differences design. We integrated data on all large roadway construction projects (defined as widening of existing roads, building new roads, improving bridges, installing intelligent transportation systems, improving intersections, and installing or upgrading traffic signals) in Texas from 2007 to 2016 with Vital Statistic data for all births with residential addresses within 1 km of construction projects. Our outcomes included term low birth weight, term birth weight, preterm birth, and very preterm birth. Using a difference-in-differences design, we included births within 3 years of construction start and 2 years of construction end. In our main model, the exposed group is limited to pregnant individuals residing within 300 m of a construction project, and the control group includes those living within 300-1000 m from a project. We used regression models to estimate the influence of construction on infant health. We included 1,360 large roadway construction projects linked to 408,979 births. During construction, we found that the odds of term low birth weight increased by 19% (95% CI: 1.05, 1.36). However, we saw little evidence of an association for other birth outcomes. Contrary to our hypothesis of decreased TRAP after construction ends, we did not observe consistent improvements post-construction for pregnant individuals living within 300 m. Continued consideration of the influence of traffic congestion programs on birth outcomes is necessary to inform future policy decisions.

Digital economy exhibits varying degrees of mitigation of air pollution in China: Total cities-economic subdivisions-urban agglomerations

Air pollution is a challenge for many cities. The digital economy enhances support for environmental pollution management, while the mechanisms and scaling heterogeneity remain unclear. This study explored the contribution of digital economy development to PM2.5 concentrations control in China and driving mechanisms in different economic subregions and urban agglomerations. Results show that the spillover transfer effect on air pollution mitigation far exceeded the direct effect at different scales. At the national scale, the air pollution mitigation effect of digital economy was mainly through empowering industrial structure optimization and green technology innovation, while it also affected economic subregions and urban agglomerations through varying scenario combinations of pathways with structural optimization, green production, resource allocation, and technology innovation. Research findings provide support for cross-regional joint management strategies of digital economy and air quality and designing regionally differentiated pollution control pathways in the digital economy dimension.

Nonlinear effect of NOx concentration decrease on secondary aerosol formation in the Beijing-Tianjin-Hebei region: Evidence from smog chamber experiments and field observations

During the COVID-19 lockdown in the Beijing-Tianjin-Hebei (BTH) region in China, large decrease in nitrogen oxides (NOx) emissions, especially in the transportation sector, could not avoid the occurrence of heavy PM2.5 pollution where nitrate dominated the PM2.5 mass increase. To experimentally reveal the effect of NOx control on the formation of PM2.5 secondary components (nitrate in particular), photochemical simulation experiments of mixed volatile organic compounds (VOCs) under various NOx concentrations with smog chamber were performed. The proportions of gaseous precursors in the control experiment were comparable to ambient conditions typically observed in the BTH region. Under relatively constant VOCs concentrations, when the initial NOx concentration was reduced to 40% of that in the control experiment (labelled as NOx,0), the particle mass concentration was not significantly reduced, but when the initial NOx concentration decreased to 20 % of NOx,0, the mass concentration of particles as well as nitrate and organics showed a sudden decrease. A "critical point" where the mass concentration of secondary aerosol started to decline as the initial NOx concentration decreased, located at 0.2-0.4 NOx,0 (or 0.18-0.44 NO2,0) in smog chamber experiments. The oxidation capacity and solar radiation intensity played key roles in the mass concentration and compositions of the formed particles. In field observations in the BTH region in the autumn and winter seasons, the "critical point" exist at 0.15-0.34 NO2,0, which coincided mostly with the laboratory simulation results. Our results suggest that a reduction of NOx emission by >60% could lead to significant reductions of secondary aerosol formation, which can be an effective way to further alleviate PM2.5 pollution in the BTH region.

In-vehicle exposure to NO2 and PM2.5: A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure

Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter.

Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the "business as usual" scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

Spatial association network of PM2.5 and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration

In this paper, we empirically study the spatial association network of PM_2.5 and the factors influencing those correlations using the gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) based on data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) in China from 2005 to 2018. We draw the following conclusions. First, the spatial association network of PM_2.5 exhibits relatively typical network structure characteristics: the network density and network correlations are highly sensitive to efforts to control air pollution, and there are obvious spatial correlations within the network. Second, cities in the center of the BTHUA have large network centrality values, while cities in the peripheral region have small centrality values. Tianjin is a core city in the network, and the spillover effect of PM_2.5 pollution in Shijiazhuang and Hengshui is the most noticeable. Third, the 14 cities can be divided into four plates, with each plate having obvious geographical location characteristics and linkage effects. The cities in the association network are divided into three tiers. Beijing, Tianjin, and Shijiazhuang are located in the first tier, and a considerable number of PM_2.5 connections are completed through these cities. Fourth, differences in geographical distance and urbanization are the main drivers of the spatial correlations of PM_2.5. The greater the urbanization differences, the more likely the generation of PM_2.5 links is, while the opposite is true for differences in geographical distance.

Estimation of exposure and premature mortality from near-roadway fine particulate matter concentrations emitted by heavy-duty diesel trucks in Beijing

Traffic exhaust is a main source of fine particulate matter (PM_2.5) in cities. Heavy-duty diesel trucks (HDDTs), the primary mode of freight transport, contribute significantly to PM_2.5, posing a great threat to public health. However, existing research based on dispersion models to simulate pollutant concentrations lacks high-spatiotemporal-resolution emission inventories of HDDTs as input data, and the public health effects of such emissions in different populations have not been thoroughly assessed. To fill this gap, we focused on Beijing as the research area and developed a high-resolution PM_2.5 emission inventory for HDDTs based on Global Navigation Satellite System-equipped vehicle trajectory data. We then simulated the fine-scale spatial distribution of diesel-related PM_2.5 and assessed the population exposure by integrating the dispersion model and population distributions. Further, we quantified the mortality attributable to noncommunicable diseases (NCDs) plus lower respiratory infections (LRIs) related to PM_2.5 emissions from HDDTs. Results showed that 3.3% of Beijing people lived in areas with high PM_2.5 HDDT emissions, which were near intercity highways. Furthermore, the estimated number of NCD + LRI annual premature deaths attributed to PM_2.5 HDDT emissions in Beijing was 339 (95% CI: 276-401). The NCD + LRI mortality increased with age, and deaths were more frequent in males than females. Our results aid the identification of HDDT PM_2.5 emission exposure hotspots for the formulation of effective mitigation measures and provide important insights into the adverse health impacts of HDDT emissions.

Quantifying factors affecting contributions of roadway exhaust and non-exhaust emissions to ambient PM10-2.5 and PM2.5-0.2 particles

Traffic-related particulate matter (PM) plays an important role in urban air pollution. However, sources of urban pollution are difficult to distinguish. This study utilises a mobile particle concentrator platform and statistical tools to investigate factors affecting roadway ambient coarse particle (PM_10-2.5) and fine particle (PM_2.5-0.2) concentrations in greater Boston, USA. Positive matrix factorization (PMF) identified six PM_10-2.5 sources (exhaust, road salt, brake wear, regional pollution, road dust resuspension and tyre-road abrasion) and seven fine particle sources. The seven PM_2.5-0.2 sources include the six PM_10-2.5 sources and a source rich in Cr and Ni. Non- exhaust traffic-related sources together accounted for 65.6% and 29.1% of the PM_10-2.5 and PM_2.5-0.2 mass, respectively. While the respective contributions of exhaust sources were 10.4% and 20.7%. The biggest non-exhaust contributor in the PM_10-2.5 was road dust resuspension, accounting for 29.6%, while for the PM_2.5-0.2, the biggest non-exhaust source was road-tyre abrasion, accounting for 12.3%. We used stepwise general additive models (sGAMs) and found statistically significant (p < 0.05) effects of temperature, number of vehicles and rush hour periods on exhaust, brake wear, road dust resuspension and road-tyre abrasion with relative importance between 19.1 and 62.2%, 12.5-42.1% and 4.4-42.2% of the sGAM model's explained variability. Speed limit and road type were also important factors for exhaust, road-tyre and brake wear sources. Meteorological variables of wind speed and relative humidity were significantly associated with both coarse and fine road dust resuspension and had a combined relative importance of 38% and 48%. The quantifying results of the factors that influence traffic-related sources can offer key insights to policies aiming to improve near-road air quality.

Simulating congestion pricing policy impacts on pedestrian safety using a system dynamics approach

Research on congestion pricing policy (CPP) impacts has generally focused on the economic and congestion-related benefits of CPPs. Few studies have examined safety effects and the interrelated factors that produce safety outcomes for vulnerable road users. We built a novel system dynamics simulation model to explore the potential mechanisms producing pedestrian injuries over time and the impacts of a CPP (and related interventions) on this trend. We found that pedestrian injury trends varied based on important decisions related to how the CPP is designed, including investments in potential safety-related supports for pedestrians. Infrastructure improvements and speed management interventions could help cities achieve both congestion-relieving goals while also improving safety. Additionally, certain CPP configurations (e.g., additional charges on for-hire vehicles) could further reduce daily vehicle trips and congestion but might lead to unintended negative safety consequences of greater pedestrian injuries. This is the first model to provide a holistic and endogenous look at how interconnected processes affecting congestion and CPP impacts also affect vulnerable road user safety. The use of system dynamics models can facilitate a holistic inspection of potential intended and unintended effects across a range of outcomes, prior to policy implementation.

Long-chain alkanes in the atmosphere: A review

As a representative species of intermediate volatile organic compounds (IVOCs), long-chain alkanes are considered to be important precursors of secondary organic aerosols (SOA) in the atmosphere. This work reviews the previous studies on long-chain alkanes in the atmosphere: (1) the detection methods and filed observations of long-chain alkanes in both gas and particle phases are summarized briefly; (2) the laboratory studies of long chain alkanes are reviewed, the kinetic data, reaction mechanism, SOA yields, and physicochemical properties of SOA are included in detail; (3) the research progress related to model simulations of long-chain alkanes are also discussed. In addition, based on available research results, several perspective contents are proposed that can be used as a guideline for future research plans.

Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM_2.5 has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM_2.5 was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM_2.5 in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM_2.5 were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM_2.5 ranged from 13.51 to 15.40 μg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R² of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R² of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM_2.5 exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

Estimation of economic costs of air pollution caused by motor vehicles in Iran (Isfahan)

Since mobile sources are one of the most important sources of air pollution, this paper tries to estimate the health effects and economic burden due to fine particulate matter (PM_2.5) concentrations from motor vehicles. In this regard, we calculate the economic costs of air pollution emitted by vehicles in Isfahan over the period from March 2018 to March 2020. The concentration of urban traffic pollution based on the generalized additive model (GAM) as well as spatial distribution of pollution is estimated. Health effects are evaluated using AirQ⁺ updated by the WHO European Centre for Environment and Health. Economic burden of mortality attributable air pollution from traffic is calculated using value of a statistical life (VOSL), and the value of life years (VOLY) approach. The results indicated that the number of deaths attributable to PM_2.5 from motor vehicles in these two consecutive years was 136 (95%CI: 89-179), and 147 cases (95%CI: 96-194), respectively. The number of years of life lost due to premature death from air pollution was 2079 years annually. The economic costs imposed under VOSL approach were on average USD 51.7 (95%CI: 43-75) million per year, and according to VOLY approach USD 11.5 (95%CI: 9-13) million per year. These results help to analyze the cost-benefit and prioritize control measures to reduce air pollution. In addition, combination of these results with other externality cost of road traffic can take account for urban transportation planning.

Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit

COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially.

Emission characteristics and control scenario analysis of VOCs from heavy-duty diesel trucks

Vehicle exhaust substantially contributes to ambient volatile organic compounds (VOCs) that imperil environmental and human health. The quantitative characterization of VOCs derived from heavy-duty diesel trucks (HDDTs) at a high spatiotemporal resolution is an important prerequisite of atmospheric quality management. However, there is little knowledge about VOC emission characteristics and accurate control policies of HDDTs owing to limited fine-grained traffic activity data. To fill this gap, this research aims to construct a link-level and hourly-based VOC emission inventory of HDDTs by combining fine-grained trajectory data, detailed vehicle specification information, localized emission factors, and underlying geographic information. The emission reduction potentials of different emission control scenarios were also evaluated. The research was conducted in Hebei Province, a predominant heavy industrial province in China. The results demonstrated that HDDTs with China 3 and below emission standards contributed to 74.85% of the HDDT generated VOC emissions, although they only accounted for 25.43% of the HDDTs operating on the road networks. The VOC emission characteristics of HDDTs were further explored at various temporal and spatial scales. Temporally, the difference between the maximum and minimum hourly VOC emissions reached 29.19%, and daily emission changes were considerably affected by holidays. Spatially, road segments with higher emission intensities and statistically significant emission hot spots were primarily distributed in intercity highways and national freeways, reflecting the contribution of high freight activity to the VOC emissions. Emission control scenario simulations demonstrated that improving HDDT emission standards can reduce VOC emissions by up to 80.06%. The results of this study contribute to a deeper understanding of the spatiotemporal patterns of VOC emissions from HDDTs and the effectiveness of emission reduction measures.

Spatial and Socio-Classification of Traffic Pollutant Emissions and Associated Mortality Rates in High-Density Hong Kong via Improved Data Analytic Approaches

Excessive traffic pollutant emissions in high-density cities result in thermal discomfort and are associated with devastating health impacts. In this study, an improved data analytic framework that combines geo-processing techniques, social habits of local citizens like traffic patterns and working schedule and district-wise building morphologies was established to retrieve street-level traffic NOx and PM2.5 emissions in all 18 districts of Hong Kong. The identification of possible human activity regions further visualizes the intersection between emission sources and human mobility. The updated spatial distribution of traffic emission could serve as good indicators for better air quality management, as well as the planning of social infrastructures in the neighborhood environment. Further, geo-processed traffic emission figures can systematically be distributed to respective districts via mathematical means, while the correlations of NOx and mortality within different case studies range from 0.371 to 0.783, while varying from 0.509 to 0.754 for PM2.5, with some assumptions imposed in our study. Outlying districts and good practices of maintaining an environmentally friendly transportation network were also identified and analyzed via statistical means. This newly developed data-driven framework of allocating and quantifying traffic emission could possibly be extended to other dense and heavily polluted cities, with the aim of enhancing health monitoring campaigns and relevant policy implementations.

The impact of the COVID-19 outbreak on the air quality in China: Evidence from a quasi-natural experiment

The outbreak of coronavirus (COVID-19) in early 2020 posed a significant threat to people's health and economic sustainability in China and worldwide. This study investigated whether the lockdown measures precipitated by the COVID-19 pandemic affected air pollutants in the short term. Moreover, we investigated the impact of the heterogeneity of cities and regions. Using city-level daily panel data for the 2018-2020 lunar calendar, we employed a two-way fixed effects model and interrupted time-series analysis to inspect the effects of the lockdown measures. Interesting empirical findings emerged from our analysis. First, compared with the base period from 2018 to 2019, the COVID-19 lockdown measures significantly reduced air pollutants. In 2020, compared to 2018, PM₁₀ and SO₂ dropped by 15.28 μg/m³ and 6.55 μg/m³, and compared to 2019, PM_2.5, PM₁₀, and SO₂ declined by 7.4 μg/m³, 19.34 μg/m³, and 1.41 μg/m³, respectively. Second, our dynamic analysis showed that as more time elapsed since the start of the lockdown, the associated reduction in air pollution became more significant. Third, the proportion of secondary industries and the cumulative number of confirmed cases had a considerable heterogeneity impact on lockdown measures. Policymakers should encourage investment in new infrastructure and initiatives to boost efficiency and enhance environmental outcomes.

Road Traffic Emission Inventory in an Urban Zone of West Africa: Case of Yopougon City (Abidjan, Côte d’Ivoire)

Road traffic emission inventories based on bottom-up methodology, are calculated for each road segment from fuel consumption and traffic volume data obtained during field measurements in Yopougon. High emissions of black carbon (BC) from vehicles are observed at major road intersections, in areas surrounding industrial zones and on highways. Highest emission values from road traffic are observed for carbon monoxide (CO) (14.8 t/d) and nitrogen oxides (NOx) (7.9 t/d), usually considered as the major traffic pollution tracers. Furthermore, peak values of CO emissions due to personal cars (PCs) are mainly linked to the old age of the vehicle fleet with high emission factors. The highest emitting type of vehicle for BC on the highway is PC (70.2%), followed by inter-communal taxis (TAs) (13.1%), heavy vehicles (HVs) (9.8%), minibuses (GBs) (6.4%) and intra-communal taxis (WRs) (0.4%). While for organic carbon (OC) emissions on the main roads, PCs represent 46.7%, followed by 20.3% for WRs, 14.9% for TAs, 11.4% for GB and 6.7% for HVs. This work provides new key information on local pollutant emissions and may be useful to guide mitigation strategies such as modernizing the vehicle fleet and reorganizing public transportation, to reduce emissions and improve public health.

Estimating Health Impacts Due to the Reduction of Particulate Air Pollution from the Household Sector Expected under Various Scenarios

Emissions from the household sector are the most significant source of air pollution in Poland, one of the most polluted countries in the EU. Estimated health impacts of the reduction of these emissions under three scenarios are presented. The EMEP4PL model and base year emission inventory were used to estimate average annual PM10 and PM2.5 concentrations with spatial resolution of 4 km × 4 km. The change in emissions under each of the scenarios was based on data from a survey on household boilers and insulation. Scenario 1 included replacement of all poor-quality coal-fired boilers with gas boilers; Scenario 2 included replacement of all poor-quality coal-fired boilers with low-emission boilers but still using solid fuels; and Scenario 3 included the thermal refurbishment of houses with the worst insulation. Impacts on the following health parameters were estimated: premature deaths (PD), Chronic Bronchitis (CB), Bronchitis in Children (BiC) and Work Days Lost (WDL). The concentration–response functions recommended by the WHO HRAPIE project were used. The analysis was conducted for two regions: Lower Silesia and Lodzkie province. The largest reduction of health impact was observed for Scenario 1. For Lower Silesia, the annual PD decreased by 1122 (34.3%), CB by 1516 (26.6%), BiC by 9602 (27.7%) and WDL by 481k (34.7%). For Lodzkie province, the largest impacts were estimated as decreases in PD by 1438 (29.9%), CB by 1502 (25.3%), BiC by 9880 (26.8%) and WDL by 669k (30.4%).

Air pollution emission effects of changes in transport supply: the case of Bogotá, Colombia

Transportation policy and planning decisions, including decisions on new infrastructure and public transport improvements, affect local and global environmental conditions. This work studies the effect of increased road capacity on traffic-related emissions in Bogotá using a tool that couples a transportation model with emission factors from COPERT IV. We followed a parametric approach varying transport supply and demand, comparing three scenarios: a baseline scenario that represents the transportation system in Bogota in 2015; scenario 1 incorporates five highway capacity-enhancement projects in Bogotá and assumes insensitive travel demand; scenario 2 incorporates the new highway projects but assumes a demand increase of 13% in vehicle trips with private cars. Results include daily and annual values of traffic-related emissions of five air pollutant criteria: CO, NO_x, PM₁₀, SO₂, and VOC for the baseline scenario, scenario 1, and scenario 2. We found a reduction in emissions after adding highway capacity and assuming inelastic demand (scenario 1). Scenario 1 results in a 15% reduction in PM₁₀ emissions and a 10% reduction in NO_x emissions. In contrast, results for scenario 2 suggest increased emissions for all air pollutant criteria (e.g., VOC and CO emissions increase by 21% and 22% compared with the baseline scenario). Therefore, new traffic demand would eliminate the emission savings observed in scenario 1 and could potentially further degrade air quality in Bogotá. While an exact estimate of induced demand that may result from highway expansion in Bogotá is not available, this analysis highlights that such projects could lead to an increase in emissions unless there is a combined effort to managing demand of private vehicle trips.

The contributions of socioeconomic indicators to global PM2.5 based on the hybrid method of spatial econometric model and geographical and temporal weighted regression

PM_2.5 pollution poses a negative effect on human health and economic growth. However, the major socioeconomic driving forces of global PM_2.5 pollution during a long-term period remained unclear. In this study, we explored the potential association between socioeconomic indicators and the PM_2.5 level worldwide using a spatial econometric model coupled with a geographical and temporal weighted regression (GTWR). The results suggested that renewable energy consumption ratio, per capita gross domestic production (GDP), per capita CO₂ emission, urban population ratio, and fossil fuel consumption ratio were major factors responsible for the global PM_2.5 pollution. The impacts of socioeconomic indicators on the PM_2.5 level varied with the income-level and time. Fossil fuel consumption ratio, per capita CO₂ emission, urban population ratio were major contributors for severe PM_2.5 pollution in the developing countries (e.g., China and India). Further, these impacts have become more remarkable in recent years. Per capita GDP still played a crucial role on the PM_2.5 pollution in India, indicating that energy-intensive industries were major contributors to its economic growth, thereby leading to the higher PM_2.5 concentration in India. However, China has strode across the inflection of Environmental Kuznets Curve (EKC) as a whole and decreased the reliance on the secondary industries. Compared with the developing countries, the impacts of socioeconomic indicators on PM_2.5 pollution in most of the developed countries remained relatively stable and weak, implicating that fossil fuel consumption and urbanization were not major contributors for local PM_2.5 level. The findings of this study clarified major contributors for PM_2.5 pollution, and provided scientific basis for mitigating the PM_2.5 pollution.

The influence of spatial patterning on modeling PM2.5 constituents in Eastern Massachusetts

Geostatistical exposure methods for air pollution have inherent uncertainties, resulting in varying levels of exposure misclassification. In this study, we propose that areas representing clusters of PM_2.5 elements are potential predictor variables to be included in spatial models for particle composition. The inclusion of these clusters may minimize the exposure misclassification. We evaluated the influence of spatial patterning on modeling of 10 components of ambient PM_2.5, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. This study was performed in three stages. First, we applied a hybrid approach (combination of Empirical Bayesian Kriging and land use regression) to estimate spatial variability for each one of the 10 components of ambient PM_2.5. In this stage, we accounted for numerous predictors representing land use, transportation, demographic, and geographical characteristics. In the second stage, we applied the same hybrid approach adding clusters of each PM_2.5 component to the set of predictor variables. The clusters here were estimated by a multivariate clustering approach based on k means. Finally, in the last stage, we compared the estimates obtained from the model without clusters (first stage) and the model with clusters (second stage). Overall, our findings suggest significant influence of spatial clusters on modeling some PM_2.5 components. We observed that the clusters may affect the error of the prediction values and especially the proportion of explained variance for most of the PM_2.5 constituents evaluated in this study. The model with cluster presented a better performance for all PM_2.5 components, except for Pb, which the R² value decreased 8.51% when we included the clusters in the analysis; and for V, which the R² value did not change with the clusters. Models for Cu and Fe explained the highest concentration variance. The R² value for the model without cluster was 0.55 for both pollutants. When we accounted for clusters, R² value increased 13 and 7% for Cu (R² = 0.62) and Fe (R² = 0.59), respectively. The models for K and S presented the lowest performance for both models with and without cluster (although the model with cluster improved substantially the R² values). Better knowledge of the influence of spatial patterns on air pollution modeling should be of interest to policy makers to devise future strategies to improve human exposure assessment to air particulates while controlling for spatial patterns of ambient PM_2.5 elemental concentration.

Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space

Numerous modeling approaches to estimate concentrations of PM_2.5 components have been developed to derive better exposures for health studies, including geostatistical interpolation approaches, land use regression models and, models based on remote sensing technology. Recently, there have been some efforts to develop models based on machine learning algorithms. Each one of these exposure assessment methods has inherent uncertainties resulting in varying levels of exposure misclassification. To date, only a few studies have attempted to systematically compare exposure estimates from different PM_2.5 constituent models. Our research addresses this gap, by comparing the predictive capabilities of ordinary geostatistical interpolation (Ordinary Kriging - OK), hybrid interpolation (combination of Empirical Bayesian Kriging and land use regression), and machine learning techniques (forest-based regression) for estimating PM_2.5 constituents in Eastern Massachusetts in the United States. We compared the estimates of 10 ambient PM_2.5 components, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. The OK model performed poorest for all PM_2.5 components, with an R² under 0.30. The hybrid model presented a slight improvement, especially for Cu and Fe, for which the R² value increased to 0.62 and 0.59, respectively. These elements presented the highest R² value from the hybrid model. The forest model presented the best performance, with R² values higher than 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. Same as observed with the hybrid model, the forest model for Cu and Fe explained the highest concentration variance, with a R² value equal to 0.88 and 0.92, respectively. The forest model for K, S, and Zn performed poorest with an R² value of 0.54, 0.37, and 0.44, respectively. The results presented here can be useful for the environmental health community to more accurately estimate PM_2.5 constituents over space.

Using rush hour and daytime exposure indicators to estimate the short-term mortality effects of air pollution: A case study in the Sichuan Basin, China

BACKGROUND

Daily mean concentrations of air pollutants have been widely used as exposure indicators to estimate the short-term mortality effects of outdoor air pollution. However, daily mean concentrations might insufficiently represent the true exposure level because of the diurnal variations of air pollutants and various human activity patterns. Daytime or rush-hour concentrations may lead to better estimations.

OBJECTIVE

Our study aimed to imitate the true exposure level under assumptions about human activity patterns and to examine the short-term mortality effects of the exposure to air pollution during a) the morning-evening rush hours (ME), b) the morning-lunch-evening rush hours (MLE), and c) the whole daytime (WDT) in Chengdu, Sichuan Basin, China.

METHODS

We investigated the diurnal variations of PM_2.5, SO₂, and O₃ and examined the associations between the three pollutants and nonaccidental mortality, cardiovascular mortality, respiratory mortality using generalized additive model. Three novel exposure indicators (ME, MLE, and WDT) were employed to imitate the most probable exposure levels. Relative change of excess risk (ER) was used to compare effects estimated from models with different exposure indicators.

RESULTS

In the relationship of PM_2.5 and mortality, ERs estimated from the novel-indicator models decreased by 4.88%-11.89% in comparison with ERs from the daily-indicator models. All the three novel indicators of SO₂ offered lower ERs of respiratory mortality than the daily indicator did. Significant associations were observed in O₃-nonaccidental mortality at lag0 in both winter and spring, and O₃-cardiovascular mortality at lag0 in winter. Overall, majority of effect estimates based on rush-hour or daytime indicators were lower than the estimates based on daily mean concentrations.

CONCLUSION

The use of daily mean concentrations may bias exposure assessment and thus inflating effect estimates. This study highlights the importance of rush-hour and daytime exposure and provides alternative indicators for estimating acute effects of air pollution.

Review of modelling air pollution from traffic at street-level - The state of the science

Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses.