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

Mapping the spatial distribution of primary and secondary PM2.5 in a multi-industrial city by combining monitoring and modeling results

Industrial cities are strongly influenced by primary emissions of PM2.5 from local industries. In addition, gaseous precursors, such as sulfur oxides (SOX), nitrogen oxides (NOX), and volatile organic compounds (VOCs), emitted from industrial sources, undergo conversion into secondary inorganic and organic aerosols (SIAs and SOAs). In this study, the spatial distributions of primary and secondary PM2.5 in Ulsan, the largest industrial city in South Korea, were visualized. PM2.5 components (ions, carbons, and metals) and PM2.5 precursors (SO2, NO2, NH3, and VOCs) were measured to estimate the concentrations of secondary inorganic ions (SO42-, NO3-, and NH4+) and secondary organic aerosol formation potential (SOAFP). The spatial distributions of SIAs and SOAs were then plotted by combining atmospheric dispersion modeling, receptor modeling, and monitoring data. Spatial distribution maps of primary and secondary PM2.5 provide fundamental insights for formulating management policies in different districts of Ulsan. For instance, among the five districts in Ulsan, Nam-gu exhibited the highest levels of primary PM2.5 and secondary nitrate. Consequently, controlling both PM2.5 and NO2 emissions becomes essential in this district. The methodology developed in this study successfully identified areas with dominant contributions from both primary emissions and secondary formation. This approach can be further applied to prioritize control measures during periods of elevated PM levels in other industrial cities.

Exploring particle concentrations and inside-to-outside ratios in vehicles: A real-time road test study

In transportation microenvironments, humans exposed to particulate matter (PM) inside vehicles can experience higher levels of daily exposure. To make inside-vehicle PM exposure measurements more feasible and easy under real driving conditions, and to quantify the relationship between the concentrations and influencing factors, we assessed PM1, PM2.5, and PM10. levels. Additionally, we collected key influencing factors to develop predictive models. The measurements of PM1, PM2.5, and PM10 concentrations showed that the ventilation setting was a significant influencing factor. The concentrations decreased significantly under the recirculation setting (RC) compared to the outside air setting (OA). The inside-to-outside (I/O) ratios of PM were 1.69 to 1.93-fold higher than those of RC under OA conditions. However, a substantial reduction in the I/O ratios was observed when RC was employed. Although both the concentrations and I/O ratios exhibited significant differences, they demonstrated strong potential relationships. PM2.5 I/O ratios accounted for over 85 % of the variation in the PM1 and PM10 I/O ratios. The developed models for the I/O ratios of PM accounted for >40 and 60 % of the variation in the measured I/O ratios for RC and OA, respectively. We used the vehicle age, vehicle interior volume, speed, cabin temperature, cabin humidity, and their higher-order terms as predictive variables. It is important to note that the influential predictive feature importance differed under RC and OA, and considering the vehicle characteristics between vehicles of the same type may be necessary when using RC. Overall, these findings indicate that the inside-vehicle PM exposure can be measured more easily under real driving conditions by considering the key influencing factors and utilizing the developed predictive models.

Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios

Ensuring environmental justice necessitates equitable access to air quality data, particularly for vulnerable communities. However, traditional air quality data from reference monitors can be costly and challenging to interpret without in-depth knowledge of local meteorology. Low-cost monitors present an opportunity to enhance data availability in developing countries and enable the establishment of local monitoring networks. While machine learning models have shown promise in atmospheric dispersion modelling, many existing approaches rely on complementary data sources that are inaccessible in low-income areas, such as smartphone tracking and real-time traffic monitoring. This study addresses these limitations by introducing deep learning-based models for particulate matter dispersion at the neighbourhood scale. The models utilize data from low-cost monitors and widely available free datasets, delivering root mean square errors (RMSE) below 2.9 μg m-3 for PM1, PM2.5, and PM10. The sensitivity analysis shows that the most important inputs to the models were the nearby monitors' PM concentrations, boundary layer dissipation and height, and precipitation variables. The models presented different sensitivities to each road type, and an RMSE below the regional differences, evidencing the learning of the spatial dependencies. This breakthrough paves the way for applications in various vulnerable localities, significantly improving air pollution data accessibility and contributing to environmental justice. Moreover, this work sets the stage for future research endeavours in refining the models and expanding data accessibility using alternative sources.

[Sustainable Urban Health as a conceptual and action approach]

The field of urban health, i.e., the application of public health for people in the city, is oriented towards both scientific knowledge and practical action. In the international arena, a scientific infrastructure exists for this purpose. Despite the common roots of public health and urban planning in Germany, the connection between these fields of work was not very apparent for a long time. Legal requirements for the participation of public health service in (urban) planning processes have had little impact so far. The aim of this study was to connect to international urban health developments in order to make the topic more visible, to support professional exchange and to give impulses for research and practice. To express the close links between human health, ecological stability and social justice, the approach was conceived as sustainable urban health. With this in mind, the program "City of the Future - Healthy, Sustainable Metropolises" was initiated in 2011.This article characterizes the basic approach as integrating and describes the underlying guiding principles, i.e., "View field expansion" as an epistemological principle and "Bridge building" as an action-guiding principle, and outlines the details. The spectrum of topics ranges from medical and nursing care over "classical" prevention and health promotion to health in all policies. Within this approach, "smaller" tasks can be pursued, e.g., an overview of local health actions and actors, networking promotion, or preserving significant developments in collective memory. At the same time, it is about contributions to the solution of "big" tasks, e.g., the derivation of conclusions from the Corona learning experience, a more consistent implementation of health in all policies, or transformation steps towards an ecologically sustainable development of society. Legal norms, public health services, health reporting, and urban planning come up as leitmotifs. The article also outlines the genesis of the position papers in this supplementary volume and concludes with an outlook.

[Mobility and Urban Health]

Cities and communities form complex microcosms in which people with very different needs and opportunities live. The structural design and functionality of urban spaces have a significant impact on individual mobility and thus on the health and quality of life of the entire population. In recent decades, politicians and municipalities have accepted negative effects on people (especially vulnerable groups) and ecosystems as a price worth paying for ensuring mobility through car-friendly structures. The interconnection of health and sustainability aspects will be a central process component for the necessary transformation of urban structures in integrated urban and transport planning. Although there are many positive framework conditions and possible solutions in the international and national context, numerous processes need to be optimized and measures implemented on a large scale. In addition, the existing tools in urban and traffic planning must be further expanded to include health aspects more comprehensively. This requires readjustments in science, in municipal practice planning, in education and in interdisciplinary funding programs.

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.

Simulating the impact of particulate matter exposure on health-related behaviour: A comparative study of stochastic modelling and personal monitoring data

Epidemiological and exposure studies concerning particulate matter (PM) often rely on data from sparse governmental stations. While low-cost personal monitors have some drawbacks, recent developments have shown that they can provide fairly accurate and fit-for-purpose data. Comparing a stochastic, i.e., agent-based model (ABM), with environmental, biometric and activity data, collected with personal monitors, could provide insight into how the two approaches assess PM exposure and dose. An ABM was constructed, simulating a PM exposure/dose assessment of 100 agents. Their actions were governed by inherent probabilities of performing an activity, based on population data. Each activity was associated with an intensity level, and a PM pollution level. The ABM results were compared with real-world results. Both approaches had comparable results, showing similar trends and a mean dose. Discrepancies were seen in the activities with the highest mean dose values. A stochastic model, based on population data, does not capture well some specifics of a local population. Combined, personal sensors could provide input for calibration, and an ABM approach can help offset a low number of participants. Implementing a function of agents influencing others transport choice, increased the importance of cycling/walking in the overall dose estimate. Activists, agents with an increased transport influence, did not play an important role at low PM levels. As concentrations rose, higher shares of activists (and their influence) caused the dose to increase. Simulating a person's PM exposure/dose in different scenarios and activities in a virtual environment provides researchers and policymakers with a valuable tool.

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.

Novel control strategies for the endocrine-disrupting effect of PAEs to pregnant women in traffic system

Traffic-related air pollution has become a global issue, and scientific regulation measures are urgently needed to reduce traffic pollution. Phthalates (PAEs) have been widely detected in the traffic environment; thus, they were chosen as target pollutants because of their endocrine-disrupting effects. The pathways of action and mechanisms of PAEs' endocrine-disrupting effects in pregnant women through inhalation were deduced. A novel whole-process 1C + 3D + 5R regulation system was developed to control the endocrine-disrupting effect of PAEs on pregnant women based on the cleaning production concept. (1) For source reduction, the 2D-QSAR model of endocrine-disrupting effects of PAEs in pregnant women was constructed to screen out the key influencing factors as hydrogen bond interaction and hydrophobic interaction. Based on this, a designed PAE substitute molecule with low volatility and endocrine-disrupting effects and no developmental toxicity was screened. The substitute molecule could reduce the volatilization amount of PAEs at the source by 41.76 %; (2) For process interception, selecting C-band UV light to eliminate PAEs molecules in the traffic environment can slow down 19.99 % of the endocrine-disrupting effect of PAEs molecules. The homology modeling method was used to design four kinds of green belt plant proteins with high PAEs absorption efficiency to absorb PAEs molecules in the traffic environment. Compared with the original green belt plant proteins, the absorption amount of PAEs increased by up to 96.08 %, and (3) For terminal prevention, dietary food schemes were designed to regulate PAEs' endocrine-disrupting effect on pregnant women. The optimal dietary food scheme was the simultaneous intake of glutamate, catechin and folic acid, which could reduce the adverse effect of PAEs on maternal and infants by 32.51 %. This study presents theoretical support for regulating PAE exposure to specific populations in the traffic environment and treating other pollutants in the future.

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy.

Light at night and the risk of breast cancer: Findings from the Sister study

BACKGROUND

Light at night (LAN) may alter estrogen regulation through circadian disruption. High levels of outdoor LAN may increase breast cancer risk, but studies have largely not considered possible residual confounding from correlated environmental exposures. We evaluated the association between indoor and outdoor LAN and incident breast cancer.

METHODS

In 47,145 participants in the prospective Sister Study cohort living in the contiguous U.S., exposure to outdoor LAN was determined using satellite-measured residential data and indoor LAN was self-reported (light/TV on, light from outside the room, nightlight, no light). We used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between outdoor and indoor LAN and breast cancer risk. Models were adjusted for age, race/ethnicity, educational attainment, annual household income, neighborhood disadvantage, latitude, and population density as a proxy for urbanicity. To evaluate the potential for residual confounding of the outdoor LAN and breast cancer relationship by factors associated with urbanicity, we considered further adjustment for exposures correlated with outdoor LAN including NO2 [Spearman correlation coefficient, rho (ρ) = 0.78], PM2.5 (ρ = 0.36), green space (ρ =  - 0.41), and noise (ρ = 0.81).

RESULTS

During 11 years of follow-up, 3,734 breast cancer cases were identified. Outdoor LAN was modestly, but non-monotonically, associated with a higher risk of breast cancer (Quintile 4 vs 1: HR = 1.10, 95% CI: 0.99-1.22; Quintile 5 vs 1: HR = 1.04, 95% CI: 0.93-1.16); however, no association was evident after adjustment for correlated ambient exposures (Quintile 4 vs 1: HR = 0.99, 95% CI: 0.86-1.14; Quintile 5 vs 1: HR = 0.89, 95% CI: 0.74-1.06). Compared to those with no indoor LAN exposure, sleeping with a light or TV on was associated with a HR = 1.09 (95% CI: 0.97-1.23) in the adjusted model.

CONCLUSIONS

Outdoor LAN does not appear to increase the risk of breast cancer after adjustment for correlated environmental exposures.

Long-term characterization of roadside air pollutants in urban Beijing and associated public health implications

Road traffic constitutes a major source of air pollutants in urban Beijing, which are responsible for substantial premature mortality. A series of policies and regulations has led to appreciable traffic emission reductions in recent decades. To shed light on long-term (2014-2020) roadside air pollution and assess the efficacy of traffic control measures and their effects on public health, this study quantitatively evaluated changes in the concentrations of six key air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO and O₃) measured at 5 roadside and 12 urban background monitoring stations in Beijing. We found that the annual mean concentrations of these air pollutants were remarkably reduced by 47%-71% from 2014 to 2020, while the concurrent ozone concentration increased by 17.4%. In addition, we observed reductions in the roadside increments in PM_2.5, NO₂, SO₂ and CO of 54.8%, 29.8%, 20.6%, and 59.1%, respectively, indicating the high effectiveness of new vehicle standard (China V and VI) implementation in Beijing. The premature deaths due to traffic emissions were estimated to be 8379 and 1908 cases in 2014 and 2020, respectively. The impact of NO₂ from road traffic relative to PM_2.5 on premature mortality was comparable to that of traffic-related PM_2.5 emissions. The public health effect of SO₂ originating from traffic was markedly lower than that of PM_2.5. The results indicated that a reduction in traffic-related NO₂ could likely yield the greatest benefits for public health.

The role of data in sustainability assessment of urban mobility policies

Data have played a role in urban mobility policy planning for decades, especially in forecasting demand, but much less in policy evaluations and assessments. The surge in availability and openness of (big) data in the last decade seems to provide new opportunities to meet demand for evidence-based policymaking. This paper reviews how different types of data are employed in assessments published in academic journals by analyzing 74 cases. Our review finds that (a) academic literature has currently provided limited insight in new data developments in policy practice; (b) research shows that the new types of big data provide new opportunities for evidence-based policy-making; however, (c) they cannot replace traditional data usage (surveys and statistics). Instead, combining big data with survey and Geographic Information System data in ex-ante assessments, as well as in developing decision support tools, is found to be the most effective. This could help policymakers not only to get much more insight from policy assessments, but also to help avoid the limitations of one certain type of data. Finally, current research projects are rather data supply-driven. Future research should engage with policy practitioners to reveal best practices, constraints, and potential of more demand-driven data use in mobility policy assessments in practice.

Modeling of PM10 emissions from motor vehicles at signalized intersections and cumulative model validation

Motor vehicle emissions especially occur at signalized intersections during idling, acceleration, and deceleration phases. The reduction of exhaust emissions from motor vehicles is on the focus of environmental studies. The main targets of this paper are the modeling of motor vehicle particulate matter (PM₁₀) emissions by American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and California Line Source for Queuing and Hot Spot Calculations (CAL3QHCR) models and investigating the effectiveness of a hypothetical green wave scenario as a pollution reduction strategy. The portion of D010 State Road in Zonguldak (Turkey) is selected. Vehicle counting is applied for determining the traffic volume. Then, the PM₁₀ emission inventory is prepared. After that, PM₁₀ pollution distribution maps at signalized intersections are created by running air quality models. Next, the CAL3QHCR model is run again for the green wave scenario which assumes free flow at signalized intersections. The maximum PM₁₀ concentrations predicted by AERMOD and CAL3QHCR models are 16.8 µg/m³ and 14.9 µg/m³, respectively. Although these values are below the threshold value, it can be said that air quality may pose a threat to public health in the existence of other sources. With the implementation of signal optimization, the PM₁₀ pollution is reduced by 10-50% at intersections. Cumulative model validation is employed including other PM₁₀ sources in the study area. PM₁₀ contribution of other sources at Zonguldak air quality monitoring station is determined by the AERMOD model. Finally, the sum of model outputs is validated against measured concentrations. According to the validation, both models are found as satisfactory and AERMOD performed better than CAL3QHCR.

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.

Emerging challenges of air pollution and particulate matter in China, India, and Pakistan and mitigating solutions

This study examines point and non-point sources of air pollution and particulate matter and their associated socioeconomic and health impacts in South Asian countries, primarily India, China, and Pakistan. The legislative frameworks, policy gaps, and targeted solutions are also scrutinized. The major cities in these countries have surpassed the permissible limits defined by WHO for sulfur dioxide, carbon monoxide, particulate matter, and nitrogen dioxide. As a result, they are facing widespread health problems, disabilities, and causalities at extreme events. Populations in these countries are comparatively more prone to air pollution effects because they spend more time in the open air, increasing their likelihood of exposure to air pollutants. The elevated level of air pollutants and their long-term exposure increases the susceptibility to several chronic/acute diseases, i.e., obstructive pulmonary diseases, acute respiratory distress, chronic bronchitis, and emphysema. More in-depth spatial-temporal air pollution monitoring studies in China, India, and Pakistan are recommended. The study findings suggest that policymakers at the local, national, and regional levels should devise targeted policies by considering all the relevant parameters, including the country's economic status, local meteorological conditions, industrial interests, public lifestyle, and national literacy rate. This approach will also help design and implement more efficient policies which are less likely to fail when brought into practice.

A review of strategies for mitigating roadside air pollution in urban street canyons

Urban street canyons formed by high-rise buildings restrict the dispersion of vehicle emissions, which pose severe health risks to the public by aggravating roadside air quality. However, this issue is often overlooked in city planning. This paper reviews the mechanisms controlling vehicle emission dispersion in urban street canyons and the strategies for managing roadside air pollution. Studies have shown that air pollution hotspots are not all attributed to heavy traffic and proper urban design can mitigate air pollution. The key factors include traffic conditions, canyon geometry, weather conditions and chemical reactions. Two categories of mitigation strategies are identified, namely traffic interventions and city planning. Popular traffic interventions for street canyons include low emission zones and congestion charges which can moderately improve roadside air quality. In comparison, city planning in terms of building geometry can significantly promote pollutant dispersion in street canyons. General design guidelines, such as lower canyon aspect ratio, alignment between streets and prevailing winds, non-uniform building heights and ground-level building porosity, may be encompassed in new development. Concurrently, in-street barriers are widely applicable to rectify the poor roadside air quality in existing street canyons. They are broadly classified into porous (e.g. trees and hedges) and solid (e.g. kerbside parked cars, noise fences and viaducts) barriers that utilize their aerodynamic advantages to ease roadside air pollution. Post-evaluations are needed to review these strategies by real-world field experiments and more detailed modelling in the practical perspective.

Estimation of Ultrahigh Resolution PM2.5 Mass Concentrations Based on Mie Scattering Theory by Using Landsat8 OLI Images over Pearl River Delta

The aerosol optical depth (AOD), retrieved by satellites, has been widely used to estimate ground-level PM2.5 mass concentrations, due to its advantage of large-scale spatial continuity. However, it is difficult to obtain urban-scale pollution patterns from the coarse resolution retrieval results (e.g., 1 km, 3 km, or 10 km) at present, and little research has been conducted on PM2.5 mass concentration retrieval from high resolution remote sensing data. In this study, a physical model is proposed based on Mie scattering theory to evaluate the PM2.5 mass concentrations by using Landsat8 Operational Land Imager (OLI) images. First, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model (which can simulate the transmission process of solar radiation in the Earth-atmosphere system and calculate the radiance at the top of the atmosphere) is used to build a lookup table to retrieve the AOD of the coast and blue bands based on the improved deep blue (DB) method. Then, the Angstrom formula is used to obtain the AOD of the green and red bands. Second, the dry near-surface AOD of four bands (coast, blue, green, red) is obtained through vertical correction and humidity correction. Third, aerosol particles are divided into four types based on the standard radiation atmosphere (SRA) model, and the optical properties of different aerosol types are analyzed to derive the volume distribution of aerosol particles. Finally, the relationship between the dry near-surface AOD of each band and the volume distribution of four aerosol particles is correlated, based on Mie scattering theory, and a physical model is established between the AOD and PM2.5 mass concentrations. Then, the distribution of PM2.5 mass concentrations is obtained. The retrieval results show that the distribution of AOD and PM2.5 at the urban scale in detail. The AOD results show that a reasonable relationship with a correlation coefficient (R2) of 0.66 and root mean square error (RMSE) of 0.1037 between Landsat8 OLI AOD and MODO4 DB AOD at 550 nm. The PM2.5 retrieval results are compared with the PM2.5 values measured by ground monitoring stations. The RMSEs for a certain day in different years, including 2017, 2018, 2019, and 2020, are 11.9470 μg/m³, 11.9787 μg/m³, 7.4217 μg/m³, and 5.4723 μg/m³, respectively. The total RMSE is 10.0224 μg/m³. The ultrahigh resolution PM2.5 results can provide pollution details at the urban scale and support better decisions on urban atmospheric environmental governance.

Spatial extent of road pollution: A national analysis

Roads form vast, pervasive and growing networks across the Earth, causing negative environmental impacts that spill out into a 'road-effect zone'. Previous research has estimated the regional and global extent of these zones using arbitrary distances, ignoring the spatial distribution and distance-dependent attenuation of different forms of road environmental impact. With Great Britain as a study area, we used mapping of roads and realistic estimates of how pollution levels decay with distance to project the spatial distribution of road pollution. We found that 25% of land was less than 79 m from a road, 50% of land was less than 216 m and 75% of land was less than 527 m. Roadless areas were scarce, and confined almost exclusively to the uplands (mean elevation 391 m), with only ca 12% of land in Great Britain more than 1 km from roads and <4% of land more than 2.5 km from roads. Using light, noise, heavy metals, NO₂, and particulate matter PM_2.5 and PM₁₀ as examples, we estimate that roads have a zone of influence that extends across >70% of the land area. Potentially less than 6% of land escapes any impact, resulting in nearly ubiquitously elevated pollution levels. Generalising from this, we find that, whilst the greatest levels of road pollution are relatively localised around the busiest roads, low levels of road pollution (which may be ecologically significant) are pervasive. Our findings demonstrate the importance of incorporating greater realism into road-effect zones and considering the ubiquity of road pollution in global environmental issues. We used Great Britain as a study area, but the findings likely apply to other densely populated regions at present, and to many additional regions in the future due to the predicted rapid expansion of the global road network.

Ambient fine particulate matter in Latin American cities: Levels, population exposure, and associated urban factors

BACKGROUND

Exposure to particulate matter (PM_2.5) is a major risk factor for morbidity and mortality. Yet few studies have examined patterns of population exposure and investigated the predictors of PM_2.5 across the rapidly growing cities in lower- and middle-income countries.

OBJECTIVES

Characterize PM_2.5 levels, describe patterns of population exposure, and investigate urban factors as predictors of PM_2.5 levels.

METHODS

We used data from the Salud Urbana en America Latina/Urban Health in Latin America (SALURBAL) study, a multi-country assessment of the determinants of urban health in Latin America, to characterize PM_2.5 levels in 366 cities comprising over 100,000 residents using satellite-derived estimates. Factors related to urban form and transportation were explored.

RESULTS

We found that about 172 million or 58% of the population studied lived in areas with air pollution levels above the defined WHO-AQG of 10 μg/m³ annual average. We also found that larger cities, cities with higher GDP, higher motorization rate and higher congestion tended to have higher PM_2.5. In contrast cities with higher population density had lower levels of PM_2.5. In addition, at the sub-city level, higher intersection density was associated with higher PM_2.5 and more green space was associated with lower PM_2.5. When all exposures were examined adjusted for each other, higher city per capita GDP and higher sub-city intersection density remained associated with higher PM_2.5 levels, while higher city population density remained associated with lower levels. The presence of mass transit was also associated with lower PM_2.5 after adjustment. The motorization rate also remained associated with PM_2.5 and its inclusion attenuated the effect of population density.

DISCUSSION

These results show that PM_2.5 exposures remain a major health risk in Latin American cities and suggest that urban planning and transportation policies could have a major impact on ambient levels.

Robust empirical Bayes approach for Markov chain modeling of air pollution index

Air pollution is a matter of concern among the public, especially for those living in urban and industrial areas. Markov chain modeling is often used to model the underlying dynamics of air pollution, which involves describing the transition probability of going from one air pollution state to another. Thus, estimating the transition probability matrix for the data of the air pollution index (API) is an essential process in the modeling. However, one may observe many zero probabilities in the transition probability matrix, especially when faced with a small sample, interpreting the results with respect to the climate condition less realistic. This study proposes a robust empirical Bayes method, which incorporates a method of smoothing the zero frequencies in the count matrix, contributing to an improved estimation of the transition probability matrix. The robustness of the empirical Bayesian estimation is investigated based on Bayes risk. The transition probability matrices estimated based on the robust empirical Bayes method for the hourly API data collected from seven monitoring stations in Malaysia for the period 2012 to 2014 are used for determining the air pollution characteristics such as the mean residence time, the steady-state probability and the mean recurrence time. Furthermore, the proposed method has been evaluated by Monte Carlo simulations. Results suggest that it is quite effective in producing non-zero transition probability estimates, and superior to the maximum likelihood method in terms of minimizing the mean squared error for individual and entire transition probabilities. Therefore, the robust empirical Bayes method proves to be an improved approach to the estimation of the Markov chain. When applied to API data, it could provide important information on air pollution dynamics that may help guiding the development of proper strategies for managing the impact of air quality.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-020-00607-4.

From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.

Dispersion behaviors of exhaust gases and nanoparticle of a passenger vehicle under simulated traffic light driving pattern

In the present study, the flow structure and pollutants dispersions were investigated by experiment and simulation on a typical passenger vehicle under simulated traffic light driving pattern. Some important findings were achieved: 1) gaseous pollutants diffuse drastically during first 0.3-0.6 m distance depending on wind velocity, at 1.25 m/s wind speed which is the similar level of exhaust gas, the pollutant concentration rises suddenly at ~0.6 m because exhaust plume is twisted by bottom gas flow, and a low velocity zone is produced; 2) as wind speed increases, the vehicle-induced turbulence is more and more important on pollutant dispersion pattern than exhaust plume dynamics. For instance, at 1.25 m/s and 4.17 m/s wind speeds, pollutants decrease to zero at ~1.6 m behind tail pipe, but at 0 m/s condition, pollutant relative fraction is still at ~0.12 level even at very long distance; 3) solid particle has larger attenuation rate than gaseous pollutants, only after ~0.6 m the particle number (PN) and diameter are very close to background values. Solid particle can diffuse to farther distance in vehicle transverse direction, when a car passes through the pedestrians with a 3 m distance, pedestrians expose to 2.6-3 times higher PN relative to atmosphere with diameters of 28-33 nm, this is very hazardous for human health; 4) exhaust pollutants disperse difficultly when followed by a car with a commonly waiting distance. At free dispersion scenario only behind ~0.6 m, PN decreases to 5800 #/cm³ (background value), but in-cabin PN of the following car (behind 0.8 m) rises to 3.5 × 10⁴ #/cm³ (even after 2-3 times decay through ventilation system). This study provides implications for future studies on transport planning.

Mapping Carbon Monoxide Pollution of Residential Areas in a Polish City

Road traffic is among the main sources of atmospheric pollution in cities. Maps of pollutants are based on geostatistical models using a digital model of the city along with traffic parameters allowing for ongoing analyses and prediction of the condition of the environment. The aim of the work was to determine the size of areas at risk of carbon monoxide pollution derived from road traffic along with determining the number of inhabitants exposed to excessive CO levels using geostatistical modeling on the example of the city of Bydgoszcz, a city in the northern part of Poland. The COPERT STREET LEVEL program was used to calculate CO emissions. Next, based on geostatistical modelling, a prediction map of CO pollution (kg/year) was generated, along with determining the level of CO concentration (mg/m3/year). The studies accounted for the variability of road sources as well as the spatial structure of the terrain. The results are presented for the city as well as divided into individual housing estates. The level of total carbon monoxide concentration for the city was 5.18 mg/m3/year, indicating good air quality. Detailed calculation analyses showed that the level of air pollution with CO varies in the individual housing estates, ranging from 0.08 to 35.70 mg/m3/year. Out of the 51 studied residential estates, the limit value was exceeded in 10, with 45% of the population at risk of poor air quality. The obtained results indicate that only detailed monitoring of the level of pollution can provide us with reliable information on air quality. The results also show in what way geostatistical tools can be used to map the spatial variability of air pollution in a city. The obtained spatial details can be used to improve estimated concentration based on interpolation between direct observation and prediction models.

High-resolution simulation of local traffic-related NOx dispersion and distribution in a complex urban terrain

Urban air pollution features large spatial and temporal variations due to the high heterogeneity in emissions and ventilation conditions, which render the pollutant distributions in complex urban terrains difficult to measure. Current urban air pollution models are not able to simulate pollutant dispersion and distribution at a low computational cost and high resolution. To address this limitation, we have developed the urban terrain air pollution (UTAP) dispersion model to investigate, at a spatial resolution of 5 m and a temporal resolution of 1 h, the distribution of the local traffic-related NO_x concentration at the pedestrian level in a 1 × 1 km² area in Baoding, Hebei, China. The UTAP model was shown to be capable of capturing the local pollution variations in a complex urban terrain at a low computational cost. We found that the local traffic-related NO_x concentration along or near major roads (10-200 μg m^-3) was 1-2 orders of magnitude higher than that in places far from roads (0.1-10 μg m^-3). Considering the background pollution, the NO and NO₂ concentrations exhibited similar patterns with higher concentrations in street canyons and lower concentrations away from streets, while the O₃ concentration exhibited the opposite behavior. Sixty percent of the NO_x concentration likely stemmed from local traffic when the background pollution level was low. Both the background wind speed and direction substantially impacted the overall pollution level and concentration variations, with a low wind speed and direction perpendicular to the axes of most streets identified as unfavorable pollutant dispersion conditions. Our results revealed a large variability in the local traffic-related air pollutant concentration at the pedestrian level in the complex urban terrain, indicating that high-resolution computationally efficient models such as the UTAP model are required to accurately estimate the pollutant exposure of urban residents.

A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data

Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO₂, O₃, PM_2.5 and PM₁₀ concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013-2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R² of 0.59, 0.72, 0.76 and 0.75 for NO₂, PM₁₀, PM_2.5 and O₃, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO₂. 50% of the population was estimated to be exposed to NO₂ concentrations between 12 and 38 μg/m³ and PM₁₀ between 20 and 35 μg/m³. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions.

How to choose healthier urban biking routes: CO as a proxy of traffic pollution

According to the World Health Organization (WHO) air pollution in urban areas, mainly associated with inhalation of gaseous pollutants and particulate matter emitted from motor vehicles, is responsible for one million deaths per year. Carbon monoxide (CO) from the incomplete combustion of fuel is known to bind with hemoglobin, decreasing the blood oxygen-delivery and inducing tissues hypoxia; being more pronounced under conditions of stress like physical activity. The present study demonstrates the usefulness of a compact CO sensor (Alphasense CO-B4) mounted on a bicycle to evaluate atmospheric levels of CO associated with urban microenvironments within a growing Australian city (Brisbane). Urban bike pathways show pronounced and significant variations in air quality according to the surrounding microenvironment and the time of day. The inhaled dose in real time and the CO total dose over each trip were valuable for estimating the air quality of the route, and identifed how the health benefits of riding a bicycle could be partially offset by poor air quality depending on where and when a cycle route is taken in the inner-city. Finally, environmental conditions, such as wind speed, were found to significantly affected atmospheric CO concentrations, at least during the study period. The present work provides information regarding commuters' exposure to atmospheric pollutants, necessary for modifying the population's (including cyclists) perception of pollution in the urban environment, providing people with the opportunity to choose a healthier route.

Association of exposure to air pollution and green space with ovarian reserve hormones levels

Exposure to air pollution has been associated with adverse health effects while exposure to greenspace has been associated with public health benefits. However, the available evidence on the association of exposure to air pollution with ovarian reserve markers is still scarce, with no study on such an association with greenspace exposure. Therefore, this study aimed to investigate the association of exposure to particulate matter with diameter of less than 1, 2.5 and 10 μm (PM₁, PM_2.5, PM₁₀), traffic indicators (distance from women's residence to major roads and total street length in different buffers around women's residential address) and greenspace indicators (residential surrounding greenspace and distance to green spaces) with serum levels of anti-müllerian hormone (AMH) and follicle stimulating hormone (FSH) as markers of ovarian reserve. This cross-sectional study was based on 67 women residing in Sabzevar, Iran (2018). Basal serum levels of FSH and AMH were measured by the enzyme-linked immunosorbent assays (ELISA). Land use regression models were used to estimate PMs concentrations at residential addresses and the average of normalized difference vegetation index (NDVI) in different buffers was used to characterize residential surrounding greenspace. Multiple linear regression models were developed to estimate the association of AMH and FSH with exposure to air pollution, traffic, and greenspace (one at a time) controlled for relevant covariates. In fully adjusted models, there was an inverse association between exposure to PM₁, PM_2.5 as well as total street length in 100 m buffer around women's residence and AMH level (β = -0.89, 95% confidence interval (CI): -1.43, -0.35, P-value ≤ 0.01, β = -1.11, 95% CI: -1.67, -0.55, P-value ≤ 0.01 and β = -0.76, 95% CI: -1.48, -0.50, P-value = 0.03, respectively). Moreover, increase in distance from home to nearest major road as well as residential surrounding greenspace (100 m buffer) and decrease in residential distance to a green space larger than 5000 m² were associated with increase in serum level of AMH. However, we did not observe any significant association between exposure to air pollution, traffic, and greenspace with FSH level. Overall, our findings supported a beneficial association of exposure to greenspace and detrimental association of exposure to air pollution with ovarian reserve.

Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping

Fine spatiotemporal mapping of PM2.5 concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM2.5 influencing factors pose challenges for accurate mapping. To address these issues, we innovatively combined social sensing data with remote sensing data and other auxiliary variables, which can bring both natural and social factors into the modeling; meanwhile, we used a deep learning method to learn the nonlinear relationships. The geospatial analysis methods were applied to realize effective feature extraction of the social sensing data and a grid matching process was carried out to integrate the spatiotemporal multi-source heterogeneous data. Based on this research strategy, we finally generated hourly PM2.5 concentration data at a spatial resolution of 0.01°. This method was successfully applied to the central urban area of Wuhan in China, which the optimal result of the 10-fold cross-validation R2 was 0.832. Our work indicated that the real-time check-in and traffic index variables can improve both quantitative and mapping results. The mapping results could be potentially applied for urban environmental monitoring, pollution exposure assessment, and health risk research.

Exposure to air pollution during pregnancy and newborn liver function

Exposure to air pollution has been associated with a wide range of adverse health outcomes. However, the available evidence on the impact of air pollution exposures on liver enzymes is still scarce. The aim of the present study was to assess the relationship between exposure to ambient PM₁, PM_2.5 and PM₁₀ during pregnancy and serum level of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) in cord blood samples of newborns. Moreover, the association between total street length in different buffers and distance to major roads at the maternal residential address and liver enzymes were investigated. This cross-sectional study was based on data from a sample of 150 newborns, from Sabzevar, Iran. Land use regression models were used to estimate concentrations of air pollutants at home during pregnancy. Multiple linear regression was developed to estimate association of AST, ALT, ALP and GGT with air pollution controlled for relevant covariates. In fully adjusted models, increase in PM₁ and PM_2.5 as well as PM₁₀ were associated with higher levels of AST, ALT and GGT. Moreover, there was a significant association between total street length in a 100 m buffer at residential address with AST, ALT and GGT. Each meter increase in distance to major roads was associated with -0.017 (95% confidence interval (CI): -0.028, -0.006) decrease in AST. Overall, our findings were supportive for association between PMs exposure during pregnancy and increase in liver enzymes in newborns. Further studies are needed to confirm these findings in other settings and populations.

Estimate annual and seasonal PM1, PM2.5 and PM10 concentrations using land use regression model

Exposure to ambient particulate matter (PM) can increase mortality and morbidity in urban area. In this study, annual and seasonal spatial pattern of PM₁, PM_2.5 and PM₁₀ pollutants were assessed using land use regression (LUR) models in Sabzevar, Iran. The studied pollutants were measured at 26 monitoring stations of different microenvironments in the study area. Sampling was conducted during four campaigns from April 2017 to February 2018. LUR models were developed based on 104 potentially predictive variables (PPVs) subdivided in six categories and 22 sub-categories. The annual mean (standard deviation) of PM₁, PM_2.5 and PM₁₀ were 36.46 (8.56), 39.62 (10.55) and 51.99 (16.25) μg/m³, respectively. The R² values and root mean square error for leave-one-out cross validations (RMSE for LOOCV) of PM₁ models ranged from 0.23 to 0.79 and 3.43-22.5, respectively. Further, R² and RMSE for LOOCV of PM_2.5 models ranged from 0.56 to 0.93 and 3.66-28.3, respectively. For PM₁₀ models the R² ranged from 0.31 to 0.82 and the RMSE for LOOCV ranged from 9.16 to 33.9. The generated models can be useful for population based epidemiologic studies and to estimate these pollutants in different parts of the study area for scientific decision making.

Understanding Spatial Variability of Air Quality in Sydney: Part 2—A Roadside Case Study

Motivated by public interest, the Clean Air and Urban Landscapes (CAUL) hub deployed instrumentation to measure air quality at a roadside location in Sydney. The main aim was to compare concentrations of fine particulate matter (PM2.5) measured along a busy road section with ambient regional urban background levels, as measured at nearby regulatory air quality stations. The study also explored spatial and temporal variations in the observed PM2.5 concentrations. The chosen area was Randwick in Sydney, because it was also the subject area for an agent-based traffic model. Over a four-day campaign in February 2017, continuous measurements of PM2.5 were made along and around the main road. In addition, a traffic counting application was used to gather data for evaluation of the agent-based traffic model. The average hourly PM2.5 concentration was 13 µg/m3, which is approximately twice the concentrations at the nearby regulatory air quality network sites measured over the same period. Roadside concentrations of PM2.5 were about 50% higher in the morning rush-hour than the afternoon rush hour, and slightly lower (reductions of <30%) 50 m away from the main road, on cross-roads. The traffic model under-estimated vehicle numbers by about 4 fold, and failed to replicate the temporal variations in traffic flow, which we assume was due to an influx of traffic from outside the study region dominating traffic patterns. Our findings suggest that those working for long hours outdoors at busy roadside locations are at greater risk of suffering detrimental health effects associated with higher levels of exposure to PM2.5. Furthermore, the worse air quality in the morning rush hour means that, where possible, joggers and cyclists should avoid busy roads around these times.

A Low-Cost Open Hardware System for Collecting Traffic Data Using Wi-Fi Signal Strength

Road traffic and its impacts affect various aspects of wellbeing with safety, congestion and pollution being of significant concern in cities. Although there have been a large number of works done in the field of traffic data collection, there are several barriers which restrict the collection of traffic data at higher resolution in the cities. Installation and maintenance costs can act as a disincentive to use existing methods (e.g., loop detectors, video analysis) at a large scale and hence limit their deployment to only a few roads of the city. This paper presents an approach for vehicle counting using a low cost, simple and easily installable system. In the proposed system, vehicles (i.e., bicycles, cars, trucks) are counted by means of variations in the WiFi signals. Experiments with the developed hardware in two different scenarios—low traffic (i.e., 400 objects) and heavy traffic roads (i.e., 1000 objects)—demonstrate its ability to detect cars and trucks. The system can be used to provide estimates of vehicle numbers for streets not covered by official traffic monitoring techniques in future smart cities.