Local Air Pollution Research Articles

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 μg/m3 to 44.8416 μg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

Exhaust Emission Assessment of Haulage Trucks at Onne Port, Nigeria

Haulage trucks are among the primary causes of air pollution in urban areas. The effect of air pollution caused by transportation, especially freight transportation, on public health is a major concern globally. In this study, emissions of haulage trucks operating at Onne Port, Rivers State in Nigeria were assessed to understand their contribution to local air pollution and environmental impact. Real-time measurements of air pollutants were taken using a portable Testo 350 Analyzer and a portable IGRESS Intelligent Detector from seventy (70) haulage trucks at Onne Port grouped according to production years, region of origin, and truck length, and the data obtained were compared with the World Health Organization (WHO) air quality standards. The results revealed that the trucks’ emissions exceeded WHO limits for six criteria pollutants, including NO2, SO2, CO, PM1, PM2.5, and PM10, which are associated with adverse health effects such as respiratory and cardiovascular diseases. Other notable findings included higher concentrations of NO2 at 35.48 ppm and PM2.5 at 300.73 µg/m3 from trucks produced between 2011 and 2021, while older trucks exhibited increased emissions of SO2 at 31.16 ppm, CO at 775.87 ppm, PM1 at 216.75 µg/m3, and PM10 at 341.75 µg/m3. There were no significant variations in pollutant emission concentrations observed within the four categories across different production years. Trucks manufactured in Asia consistently emitted lower pollutant concentrations compared with those from other regions, suggesting the influence of varying emission standards. Interestingly, truck length did not significantly impact emission levels. These findings underscore the urgency of addressing the air quality issues associated with haulage trucks in the region, highlighting the need for stringent emissions control measures.

Overestimated environmental benefits of short-term changes in mobility behaviour: The case of Berlin during COVID-19

The COVID-19 pandemic changed urban mobility patterns in unprecedented ways with movement decreasing, few public transport customers, and empty streets turning into pop-up bike lanes. Accordingly, a decrease in local air pollution was reported at the start of the pandemic and models suggested a lower carbon footprint of urban mobility. Here, we offer a critical discussion of the indirect environmental impacts that can be inferred from short-lived mobility behaviour. Examining Berlin, we analyse to what extent transport volume and modal split influenced direct and indirect greenhouse gas emissions throughout the pandemic (2020–2022). The analysis combines traffic counts and survey data with process-specific life cycle assessment factors. The results show that reductions in the carbon footprint of mobility during the pandemic stem largely from a short-term decline in car use, while the modal split became more carbon intensive. Emission reductions from lower use of public transport were not realised, nor a reduction of indirect emissions from vehicle production due to slowly responding upstream demand and public transport planning. This highlights the need to revise the current assumption of perfectly responding markets in environmental footprint assessment of short-term behavioural change during crises, urban experimentation, and transitions.

Evaluating emission reduction potential and co-benefits of CO2 and air pollutants from mobile sources: A case study in Shanghai, China

Mobile sources have significantly contributed to local carbon dioxide (CO2) and air pollutants (APs) emissions, but researches on their overall co-control remain limited. This study, aiming at Shanghai's overall mobile sources, conducted the prediction of emission reduction potential, co-benefits assessment and peak CO2 emissions analysis. Results revealed a reduction of 55.9% in CO2 emissions and 64.1% in APs equivalents by 2050 under the strictest scenario. Without additional mitigation, CO2 total emissions in 2050 would approach 120 Mt. Motor vehicles contribute the most to CO2 and CO emissions, while ships serve as the primary source of other APs. Motor vehicles' CO2 emissions could peak in 2033 (42.57∼47.47 Mt), with the overall mobile sources achieving peak emissions two years later (56.21∼63.55 Mt). VOCs, SO2, NOx, and CO2 exhibit relatively high synergy, and the emission reduction degree of NOx would be higher than that of CO2, while other pollutants are the opposite.

The association of exhaled nitric oxide with air pollutants in young infants of asthmatic mothers

BackgroundExhaled nitric oxide is a marker of airway inflammation. Air pollution induces airway inflammation and oxidative stress. Little is known about the impact of air pollution on exhaled nitric oxide in young infants.MethodsThe Breathing for Life Trial recruited pregnant women with asthma into a randomised controlled trial comparing usual clinical care versus inflammometry-guided asthma management in pregnancy. Four hundred fifty-seven infants from the Breathing for Life Trial birth cohort were assessed at six weeks of age. Exhaled nitric oxide was measured in unsedated, sleeping infants. Its association with local mean 24-h and mean seven-day concentrations of ozone, nitric oxide, nitrogen dioxide, carbon monoxide, sulfur dioxide, ammonia, particulate matter less than 10 μm (PM10) and less than 2.5 μm (PM2.5) in diameter was investigated. The air pollutant data were sourced from local monitoring sites of the New South Wales Air Quality Monitoring Network. The association was assessed using a ‘least absolute shrinkage and selection operator’ (LASSO) approach, multivariable regression and Spearman’s rank correlation.ResultsA seasonal variation was evident with higher median exhaled nitric oxide levels (13.6 ppb) in warmer months and lower median exhaled nitric oxide levels (11.0 ppb) in cooler months, P = 0.008. LASSO identified positive associations for exhaled nitric oxide with 24-h mean ammonia, seven-day mean ammonia, seven-day mean PM10, seven-day mean PM2.5, and seven-day mean ozone; and negative associations for eNO with seven-day mean carbon monoxide, 24-h mean nitric oxide and 24-h mean sulfur dioxide, with an R-square of 0.25 for the penalized coefficients. These coefficients selected by LASSO (and confounders) were entered in multivariable regression. The achieved R-square was 0.27.ConclusionIn this cohort of young infants of asthmatic mothers, exhaled nitric oxide showed seasonal variation and an association with local air pollution concentrations.

Biodiesel production via transesterification reaction

Global warming, climate change, local air pollution, ozone depletion, acid rain and depletion of fossil fuel are the major global environmental issues. Presently, non-renewable fossil fuels meet up to 80% of the world’s energy demand. Rapid consumption of fossil fuels as well as rising environmental pollution caused by extreme CO2 emissions, sulphur dioxide, and aromatic hydrocarbons has become crucial in searching for a clean and renewable energy source such as biodiesel. With continuous growth in the world’s population, rapid industrialization, urbanization, and economic growth, fossil fuel consumption is escalating to meet the global energy demand. Biodiesel is renewable, non-toxic, environment-friendly and an economically feasible options to tackle the depleting fossil fuels and its negative environmental impact. Biodiesel is an alternative fuel made by transesterification reaction of oil and alcohol under the influence of catalyst. Biodiesel is an alcoholic ester of various fatty acids, also known as FAME (Fatty Acid Methyl Esters). In the process of transesterification, the performance of the catalyst is the key factor of the biodiesel yield. Catalysts used in biodiesel production are classified as homogeneous, heterogeneous, and enzymatic catalysts. The incorporation of a catalysis-based transesterification reaction generally increases the rate of reaction and enhances the yield of the end product. In case of homogeneous catalysts, there are problems of catalyst poisoning and contamination. Heterogeneous acid catalysis has a lesser toxic effect and gives rise to fewer environmental problems compared to the homogeneous mode. The cost of each specific catalyst depends on various factors, including the source, synthesis method, and its reusability. Generally, using waste or biomass as sources of catalysts may reduce the price of commercially available solid catalysts.

A population-based cohort study of electronic tolling, traffic congestion, and adverse birth outcomes

BackgroundAlthough traffic-related air pollution is largely regulated at the federal level, congestion reduction projects may reduce local traffic and air pollution to levels that create positive co-benefits for population health. In recent years, many urban areas have implemented electronic tolling systems to improve traffic conditions. ObjectiveQuantify associations between implementing electronic tolling and local changes in traffic and infant health. MethodsUsing a population-based birth cohort (Texas, 1999–2016), we calculated residential proximity to the nearest tolled road segment within 5 km (n = 625,279) and examined changes in local traffic before and after toll implementation. Using a difference-in-differences design, we compared four markers of adverse birth outcomes (term birth weight, term low birth weight, preterm birth, very preterm birth) among infants from pregnant people residing < 0.5 km from a road segment before and after the tolls were implemented and compared them to a contemporaneous population of pregnant people residing at 2–5 km. ResultsWe observed minimal changes in local traffic after the implementation of tolling. Among births within 500 m of a tolled road, we found little evidence of an association between the implementation of tolling and adverse birth outcomes (term birth weight [β: −4.5, 95 % CI: −11.7, 2.6], term low birth weight [OR: 1.00, 95 % CI: 0.89, 1.13], preterm birth [OR: 0.99, 95 % CI: 0.92, 1.05], very preterm birth [OR: 1.00, 95 % CI: 0.84, 1.18]), compared to the contemporaneous control group of births at 2–5 km. In sub-analyses, we found some evidence of a reduced association between toll booth removal and preterm birth (OR: 0.84, 95 % CI: 0.70, 1.01) but not for other outcomes or tolling types. DiscussionIn this large population-based retrospective cohort study of births in Texas, we found little evidence that the implementation of tolling was consistently associated with improvements in local infant health outcomes.

Does green foreign investment contribute to air pollution abatement? Evidence from China

AbstractTo better balance economic growth and environmental protection, China is encouraging green foreign direct investment (FDI), but the environmental impact remains unclear. Our study examines the environmental impact of green FDI at small spatial scales using a unique dataset. Results show that green FDI significantly alleviates local air pollution. This effect is more pronounced in the vicinity of green FDI enterprises' sites and decays as the distance increases. Also, it is more salient in areas where local governments highlight environmental protection. Heterogeneous analysis finds that the effect is more significant for enterprises that are small‐scale, sole‐venture or in manufacturing industries.

Air pollution trapping in the Dresden Basin from gray-zone scale urban modeling

Abstract. The microscale variability of urban air pollution is essentially driven by the interaction between meteorology and urban topography, which remains challenging to represent spatially accurately and computationally efficiently in urban dispersion models. Natural topography can additionally exert a considerable amplifying effect on urban background pollution, depending on atmospheric stability. This requires an equally important representation in models, as even subtle terrain-height variations can enforce characteristic local flow regimes. In this model study, the effects of urban and natural topography on the local winds and air pollution dispersion in the Dresden Basin in the Eastern German Elbe valley are investigated. A new, efficient urban microscale model is used within a multiscale air quality modeling framework. The simulations that consider real meteorological and emission conditions focus on two periods in late winter and early summer, respectively, as well as on black carbon (BC), a key air pollutant mainly emitted from motorized traffic. As a complement to the commonly used mass concentrations, the particle age content (age concentration) is simulated. This concept, which was originally developed to study hydrological reservoir flows in a Eulerian framework, is adapted here for the first time for atmospheric boundary-layer modeling. The approach is used to identify stagnant or recirculating orographic air flows and resulting air pollution trapping. An empirical orthogonal function (EOF) analysis is applied to the simulation results to attribute the air pollution modes to specific weather patterns and quantify their significance. Air quality monitoring data for the region are used for model evaluation. The model results show a strong sensitivity to atmospheric conditions, but generally confirm increased BC levels in Dresden due to the valley location. The horizontal variability of mass concentrations is dominated by the patterns of traffic emissions, which overlay potential orography-driven pollutant accumulations. Therefore, an assessment of the orographic impact on air pollution is usually inconclusive. However, using the age-concentration metric, which filters out direct emission effects, previously undetected spatial patterns are discovered that are largely modulated by the surface orography. The comparison with a dispersion simulation assuming spatially homogeneous emissions also proves the robustness of the orographic flow information contained in the age-concentration distribution and shows it to be a suitable metric for assessing orographic air pollution trapping. The simulation analysis indicates several air quality hotspots on the southwestern slopes of the Dresden Basin and in the southern side valley, the Döhlen Basin, depending on the prevailing wind direction.

Air quality and health effects of a transition to ammonia–fueled shipping in Singapore

Ammonia has been proposed to replace heavy fuel oil (HFO) in the shipping industry by 2050. When produced with low-carbon electricity, ammonia can reduce greenhouse gas emissions. However, ammonia emissions also contribute to local air pollution via the formation of secondary particulate matter. We estimate the potential ammonia emissions from storage and bunkering operations for shipping in Singapore, a port that accounts for 20% of global bunker fuel sales, and their impacts on air quality and health. Fuel storage and bunkering can increase total gaseous ammonia emissions in Singapore by up to a factor of four and contribute to a 25%–50% increase in ambient PM2.5 concentration compared to a baseline scenario with HFO, leading to an estimated 210–460 premature mortalities in Singapore (30%–70% higher than the baseline). Proper abatement on storage and bunkering can reduce these emissions and even improve ambient PM2.5 concentrations compared to the baseline. Overall, while an energy transition from HFO to ammonia in the shipping industry could reduce global greenhouse gas and air pollutant burdens, local policies will be important to avoid negative impacts on the communities living near its supply chain.

Bitcoin and carbon dioxide emissions: Evidence from daily production decisions

Environmental externalities from cryptomining may be large, but have not been linked causally to mining incentives. We exploit daily variation in Bitcoin price as a natural experiment for an 86 megawatt waste coal-fired power plant with on-site cryptomining. We find that carbon emissions respond swiftly to mining incentives, with price elasticities of 0.69–0.71 in the short-run and 0.33–0.40 in the longer run. A $1 increase in Bitcoin price leads to $3.11–$6.79 in external damages from carbon emissions alone, well exceeding cryptomining’s value added (using a $190 social cost of carbon, but ignoring increased local air pollution). As cryptomining requires ever more computing power to mine a given number of blocks, our study highlights both the revitalization of US fossil assets and the need for financial industry accounting to incorporate cryptomining externalities.

Carbon footprint of Nepalese healthcare system: A study of Dhulikhel Hospital.

Though direct greenhouse gas emissions cannot be observed in health care sectors, there can exist indirect emissions contributing to global climate change. This study addresses the concept of the carbon footprint and its significance in understanding the environmental impact of human activities, with a specific emphasis on the healthcare sector through gate-to-gate (GtoG) life cycle assessment. Transportation, energy consumption, and solid waste generated by hospitals are the primary sources of carbon emissions. Different standards, guidelines and parameters were used to estimate emissions from both the primary and secondary data. All steps and sub-steps involved in GtoG were accessed and analyzed within the standard ISO 14040:44 guideline. An extensive review of existing literature was carried out for the evaluation and verification of secondary data. The total carbon footprint of generators, electricity consumption, transportation activities, LPG cylinders, PV systems was found to be 58,780 kg-CO2-eq/yr, 519,794 kg-CO2-eq/yr, 272,375 kg-CO2-eq/yr, 44,494 kg-CO2-eq/yr, 35,283 kg-CO2-eq/yr respectively and the emissions from non-biodegradable solid waste was found to be 489,835 kg-CO2/yr. Local air pollutants such as PM 10, CO, SO 2, NO X, and VOCs generated by generators and transportation were also estimated. The CH 4 emissions from liquid waste were 1177.344 kg CH 4/BOD yr, and those from biodegradables were 3821.6954 kg CH4/yr. Healthcare professionals and policymakers can take action to reduce the sector's carbon footprint by implementing best practices and encouraging sustainable behavior. This study can be taken as foundation for further exploration of indirect emissions from healthcare sectors not only in Nepal but also in south Asian scenario.

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

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

The performance of artificial neural networks for modeling daily concentrations of particulate matter from meteorological data.

The use of techniques based on artificial intelligence and machine learning for the simulation of many processes is becoming increasingly important in environmental sciences, with applications in the study of time series of atmospheric properties, such as pollution levels. The present work aimed to evaluate the efficiency of a model based on Artificial Neural Networks (ANN) in the simulation PM10 from meteorological data observed between 2018 and 2019 in Guaíba, southern Brazil, thus also having an estimate of the influence of atmospheric conditions on local air pollution. For this purpose, meteorological and PM10 data obtained from the stations Parque 35, sustained by Celulose Riograndense (CMPC), and A-801, sustained by the National Institute of Meteorology (INMET), were used. The ANN used for the simulation was of the Multilayer Perceptron type, trained by the backpropagation algorithm with cross-validation. The results obtained indicate that the simulation was satisfactory with a Nash-Sutcliffe index (NSE) of 0.64, a linear correlation coefficient (R) of 0.81, a relative error (Er) of 26% and a root mean square error (RMSE) of 7.40µg/m3. Thus, even with some difficulty in estimating extreme concentrations, the model was suitable for the largest range observed, of 10µg/m3 to 50µg/m3. For this dataset, the model proved to be an useful assessment tool and has the potential to be applied operationally to contribute to the monitoring and control of air quality levels both in the study area and in other regions of Brazil and the world.

Different volatile organic compounds in local point source air pollution pose distinctive elevated risks for respiratory disease-associated emergency room visits

Air pollution increases risk of respiratory disease but prior research has focused on particulate matter and criteria air pollutants, and there are few studies on respiratory effects of volatile organic compounds (VOC). We examined zip code level relationships between emergency room (ER) visits for respiratory illness and VOC pollution in New York State from 2010 to 2018. Detailed information on VOC pollution was derived from the National Emissions Inventory, which provides point source information on VOC emissions at the zip code level. We considered four respiratory diseases: asthma, acute upper respiratory infections, chronic obstructive pulmonary disease (COPD), and lower respiratory disease, using mixed effects regression with a random intercept to account for county level variability in single pollutant models, and Random Forest Regression (RFR) to assess relative importance of VOC exposures when considered together in multipollutant models.Single pollutant models show associations between respiratory-related ER visits with all pollutants of interest across all study years, even after adjusting for poverty and smoking by zip code. The largest relative single pollutant effect sizes considered included benzene, ethylbenzene, and total (summed) VOCs. Results from RFR including all VOC exposures indicate that ethylbenzene has the greatest variable importance for asthma, acute upper respiratory infections, and COPD, with toluene and benzene most important for lower respiratory ailments. RFR results also demonstrate presence of pairwise interactive effects between VOC pollutants. Our findings show that local VOC pollution may offer a significant contribution to the risk of respiratory disease-related ER visits, and that effects vary by illness and by VOC compound. ER visit rates for respiratory illness were elevated in high poverty zip codes, although this may be attributable to the fact that the poor lack basic access to health care and use ERs more frequently for routine care.

From lowland plains to the Altiplano: The impacts of regional transport of wildfire smoke on the air quality of Bolivian cities

Wildfires emerged as a global concern in recent decades, because they deteriorate the air quality in urban centres situated downwind, impact human health, ecosystems and regional climate. In this study, we assessed the air quality of Bolivia's three largest metropolitan areas during the period 2017–2019 by analysing in situ criteria air pollutant data (PM10, O3 and NOx) coupled with satellite (aerosol optical depth AOD and total CO column), fire foci data, and airmass backward trajectories. The Bolivian lowland plains were highly impacted by smoke transported from the Amazon and Cerrado biomes. Due to its proximity to the fire foci, Santa Cruz was the most affected city, where PM10, CO and AOD increased between 1.3- and 3.9-fold in September (maximum fire activity) compared to April (lowest fire activity). In Cochabamba and La Paz-El Alto, the impacts of wildfires on AOD and CO were lower (15–16% increase). A case study of regional smoke in September 2019 using Cochabamba as an example revealed that: i) airmass trajectories passed over regions with high fire radiative power density in southern Bolivia, Paraguay and northern Argentina; ii) the advection of wildfire smoke caused unusual episodes of enhanced nocturnal O3 concentrations (20–45 μg/m3) over a four-day pollution event. The results indicated that local sources (e.g., motorised traffic, brick kilns and dust resuspension) largely contributed to deteriorate the air quality, particularly in the dry months, surpassing the World Health Organization recommendations for PM10, in La Paz-El Alto and Cochabamba. We highlight the need of international cooperation among South American countries to combat fire practices in the region, given the transboundary nature of the biomass burning plumes. Moreover, it is crucial to implement local measures aimed at tackling local air pollution sources, especially in a changing climate in which drier conditions can favour the onset and propagation of wildfires and accumulation of air pollutants.

Short-term exposure to various ambient air pollutants and emergency department visits for cause-stable ischemic heart disease: a time-series study in Shanghai, China

Studying the impact of local meteorological conditions and air pollution on cardiovascular disease is crucial for reducing the burden of cardiovascular disease. However, there have been few studies on the acute effects of various air pollutants on stable ischemic heart disease (SIHD), and the effects of these factors are not well defined and require further investigation. We performed a time-series study aimed at exploring the association between short-term exposure to various air pollutants and emergency department (ED) visits for SIHD during 2013–2020 in Baoshan District Renhe Hospital of Shanghai, China. The associations between air pollution (NO2, PM2.5, PM10, SO2 O3-8 h and CO) and ED visits were analyzed using quasi-Poisson regression. Subgroup and sensitivity analyses were conducted. From 2013 to 2020, a total of 18,241 ED visits for SIHD were recorded. Elevated PM2.5, PM10, NO2, SO2 and CO were significantly associated with increased ED visits for SIHD at lag (0, 5), lag 0, lag (0-4, 01-03), lag (0-3, 5, 01-03) and lag (3-5). When the concentration of O3-8 h was lower than the threshold recommended by the WHO, exposure to O3-8 h was associated with a slightly decreased risk of SIHD. Moreover, the relationship between different types of air pollution and the frequency of ED visits exhibited variations based on gender, age, and seasonality. This study suggests that short-term exposure to PM2.5, PM10, NO2, SO2 and CO might induce SIHD, especially in old females. Air pollution control measures should be encouraged to prevent the occurrence and development of SIHD.

Heterogeneous impacts of local traffic congestion on local air pollution within a city: Utilizing taxi trajectory data

This study estimates the causal effects of traffic congestion on air quality in local areas within a city, highlighting the spatial and temporal variation in local traffic, local air pollution, and health risks to residents due to local pollutant exposure. We employ a large-scale taxi driving trajectory data to construct measures of traffic congestion in local areas surrounding air quality monitoring stations in Beijing, on an hourly basis. The station-by-hour panel data enables the estimation of both contemporaneous and lagged marginal effects of local traffic on local air pollution. We find that local traffic congestion increases concentrations of NO2 and CO contemporaneously, but concentrations of PM2.5 and PM10 in a 2-h lagged manner. The magnitude and significance of the estimates vary with local traffic and pollution conditions, implying heterogeneous marginal effects on local air pollution of local traffic across locations, hours of the day, months and seasons. These results stress the importance of designing traffic policies that are both spatially and temporally fine-grained to attain efficiency and equity.

The effects of cash for clunkers on local air quality

We study the effects of a large car scrappage scheme in Germany on new car purchases and local air quality by combining vehicle registration data with data on local air pollutant emissions. For identification we exploit cross-sectional variation across districts in the number of cars eligible for scrappage. The scheme had substantial effects on car purchases and did not simply reallocate demand across time in the short-term. Nevertheless, about half of all subsidized buyers benefited from windfall gains. The renewal of the car stock improved local air quality suggesting substantial mortality benefits that likely exceed the cost of the policy. While policy take-up is somewhat smaller in urban districts, improvements in air quality and health tend to be larger due to a higher car density.

Rush hour-and-a-half: Traffic is spreading out post-lockdown.

Traffic congestion is ubiquitous in major cities around the world. Congestion is associated with a slew of negative effects, including delays and local air pollution. Because of the negative effects of congestion, governments invest billions of dollars into the highway system to try to reduce congestion and accommodate peak-hour automobile travel demand. The COVID-19 pandemic presented a significant disruption to transportation systems globally. One impact was a drastic reduction in travel, leading to free-flowing traffic conditions in many previously-congested cities. As lockdowns eased, traffic volumes returned to near-normal levels. However, the temporal pattern of demand may differ, due to increased remote work or other factors. In this article, we examine the temporal distribution of highway demand in California, using data from over 3,500 traffic sensors. We find that peak-hour automobile travel is spreading in the post-lockdown period. In addition to decreased traffic congestion, this finding also has implications for infrastructure investment. Roadways are generally sized based on peak-hour demand. As the peaks spread, some highway construction project may prove unnecessary. It may be possible to reallocate road space to other uses with fewer tradeoffs in terms of traffic congestion.