Effect of public transport strikes on air pollution levels in Barcelona (Spain)

Advancing nuanced pollution control: Local improvements and spatial spillovers of policies on key enterprises

This paper examines the impact of air pollution control policies targeting key polluting enterprises, highlighting a strategic shift towards precision pollution control that concentrates on high-emission, high-risk businesses. The paper explores the efficacy of these policies and their potential spatial spillover effects, utilizing panel data from 259 Chinese cities from 2013 to 2021. Employing the difference-in-differences (DID) model and spatial Durbin model, the study analyzes both the direct local effects and the broader spatial consequences of these regulatory measures on air quality. The findings indicate a significant reduction in air pollutant concentrations in urban areas, attributing this improvement to factors such as industrial restructuring, increased investment in science and technology, and economic growth. Spatial econometric analysis further reveals a substantial positive correlation in air quality among Chinese cities. However, estimates of the spillover effect indicate that while such policies successfully reduce pollution locally, they could unintentionally degrade air quality in adjacent areas. The study highlights the need for nuanced policy strategies to mitigate unintended spatial spillovers and enhance overall effectiveness. It recommends tailored policies that integrate environmental and socioeconomic objectives, national and regional coordination for consistent enforcement, technology-driven compliance strategies, and incentives for sustainable enterprise practices.

Invasion of privacy or structural violence? Harassment against women in public transport environments: A systematic review

BACKGROUND

Despite being neglected for several decades, and in many countries, public transport environments have transformed into a reflection of social disparities and inequalities. Among these issues, harassment-a pervasive and worldwide gendered dynamic-has been demonstrated to negatively impact women's behavioral trends, daily opportunities, and health impacts, as well as safety and security outcomes.

AIM

This systematic review aimed to examine a set of studies analyzing harassment against women in public transport environments, with a focus on key issues such as its prevalence, impact on transport dynamics, preventive strategies, and policing avenues documented in the scientific literature.

METHOD

A total of 575 indexed articles were filtered using the PRISMA methodology, resulting in a final selection of 28 original articles directly addressing the issue up to December 2023. Search strategies were developed and implemented across WOS, Scopus, NCBI, Google Scholar, and APA databases.

RESULTS

Besides high frequency, widespread underreporting, and adverse effects on women's safety, this review has identified correlations between harassment and travel behavioral adaptations. Furthermore, it reveals a noticeable disparity between the existing measures and those perceived as more effective by potential victims. These findings underscore the pressing need to listen to and promote the inclusion of women in decision-making regarding transport affairs.

CONCLUSION

The findings of this systematic review suggest that, despite a slightly limited body of research, the impact of transport harassment on women's health and welfare is consistently supported in the literature. In addition to being largely explained by existing inequalities rooted in social determinants, transit harassment further exacerbates gender gaps, gaining prospective importance for transport settings.

Subways and CO2 emissions: A global analysis with satellite data

This paper estimates a global CO2 emissions model using satellite data at 25 km resolution. The model incorporates industrial sources (including power, steel, cement, and refineries), fires, and non-industrial population-related factors associated with household incomes and energy requirements. It also tests the impact of subways in the 192 cities where they operate. We find highly significant effects with the expected signs for all model variables, including subways. In a counterfactual exercise estimating CO2 emissions with and without subways, we find they have reduced population-related CO2 emissions by about 50 % for the 192 cities and about 11 % globally. Extending the analysis to future subways for other cities, we estimate the magnitude and social value of CO2 emissions reductions with conservative assumptions about population and income growth and a range of values for the social cost of carbon and investment costs. Even under pessimistic assumptions for these costs, we find that hundreds of cities realize a significant climate co-benefit, along with benefits from reduced traffic congestion and local air pollution, which have traditionally motivated subway construction. Under more moderate assumptions, we find that, on climate grounds alone, hundreds of cities realize high enough social rates of return to warrant subway construction.

Quantifying spatially varying impacts of public transport on NO[Formula: see text] concentrations with big geo-data

Anthropogenic NO[Formula: see text] concentrations cause climate change and human health issues. Previous studies have focused on the contribution of traffic factors to NO[Formula: see text] emissions but have ignored the spatially varying impact of public transport supply and demand on high-resolution NO[Formula: see text] concentrations. This study first applies a two-stage interpolation model to generate a high-resolution urban NO[Formula: see text] concentration map originating from satellite measurement products. Then, we formulate 12 explanatory indicators derived from a fusion of massive big geo-data including smart card data and point of interest information, to represent the specific degree of public transport supply and citizens' demand. Furthermore, a geographically weighted regression is applied to quantify the spatial variation in the effect of these indicators on the urban NO[Formula: see text] concentrations. The result shows that public transportation coverage, frequency, and capabilities as public transport supply indicators in metropolitan and suburban areas have a two-way influence on the NO[Formula: see text] emissions. However, among public transport demand indicators, the economic level has a significant positive impact in most areas. Our findings can provide policy implications for public transportation system optimization and air quality improvement.

The Effect of Commuting Time on Quality of Life: Evidence from China

This study examines the effect of commuting time on quality of life. We find that the longer the commute time workers use, the lower satisfaction with work and life they have; the long commute also causes health damage, affecting physical health and causing inactivity. However, better public transportation infrastructure can decrease commuting time, especially the construction of subways.

Assessment of PM2.5-related health effects: A comparative study using multiple methods and multi-source data in China

In China, PM_2.5 pollution has caused extensive death and economic loss. Thus, an accurate assessment of the spatial distribution of these losses is crucial for delineating priority areas for air pollution control in China. In this study, we assessed the PM_2.5 exposure-related health effects according to the integrated exposure risk function and non-linear power law (NLP) function in 338 prefecture-level cities in China by utilizing online monitoring data and the PM_2.5 Hindcast Database (PHD). Our results revealed no significant difference between the monitoring data and PHD (p value = 0.66 > 0.05). The number of deaths caused by PM_2.5-related Stroke (cerebrovascular disease), ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer at the national level estimated through the NLP function was 0.27 million (95% CI: 0.06-0.50), 0.23 million (95% CI: 0.08-0.38), 0.31 million (95% CI: 0.04-0.57), and 0.31 million (95% CI: 0.16-0.40), respectively. The total economic cost at the national level in 2016 was approximately US$80.25 billion (95% CI: 24.46-132.25). Based on a comparison of Z statistics, we propose that the evaluation results obtained using the NLP function and monitoring data are accurate. Additionally, according to scenario simulations, Beijing, Chongqing, Tianjin, and other cities should be priority areas for PM_2.5 pollution control to achieve considerable health benefits. Our statistics can help improve the accuracy of PM_2.5-related health effect assessments in China.

Assessment of the Road Traffic Air Pollution in Urban Contexts: A Statistical Approach

In the article, a statistical approach to the assessment of the emission rates discharged by road traffic in a spatial context is proposed. It exploits the ‘yearly average vehicle’, an indicator representing the pollutant emission rate of the average vehicle belonging to a specific category, considering the statistical variability of most of the involved traffic parameters: the vehicle speed and mileage travelled in the considered time. Finally, indicators assessing both the most probable value among the possible emission rates and the extent of their variability range are proposed. They may also be used to underpin decision-making processes, when the effects of different policies addressing air pollution issues are to be evaluated. Therefore, they are suitable for the analysis supporting urban planning activities, with a view to addressing and mitigating the effects and the consequences of pollution due to the transportation sector of the urban context. In addition, they can also be exploited by researchers when prediction analyses are to be performed.

The effect of national protest in Ecuador on PM pollution

Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall - 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

When particulate matter strikes cities: Social disparities and health costs of air pollution

We investigate the heterogeneous effects of particle pollution on Italian daily hospitalizations and their costs by exploiting public transportation strikes as plausibly-exogenous shocks in pollution exposure. We find that a one standard deviation increase in PM₁₀ causes additional 0.79 hospitalizations per 100,000 residents, and the effect is stronger for the elderly, low educated individuals and migrants. Furthermore, we find that young individuals, an arguably healthy age group, exhibit economically meaningful responses to air pollution with an effect ranging between 0.45 and 1.04. Our results imply a large role of avoidance behavior driving heterogeneous marginal health effects. Total daily costs of a one standard deviation increase in PM₁₀ represent 0.5% of the total daily health expenditure, and 85% of this additional spending comes from more patients hospitalized, while the remaining 15% can be attributable to more costly, and likely more complex, hospitalizations.

Indoor-outdoor relationships of airborne nanoparticles, BC and VOCs at rural and urban preschools

Health risks caused by exposure to black carbon (BC) and nanoparticles (NP) are well studied, although no standard currently exists for them worldwide. Exposure to children may lead to serious health effects due to their increased vulnerability and longer time spend inside the classrooms, making it important to assess the factors that affect air quality in preschools. Thus, this work aims to evaluate indoor-outdoor (I/O) relationships of NPs in the 10-420 nm range, BC and volatile organic compounds (VOCs) at rural and urban preschools (aged 3-5 years) between May 2016 and July 2017. Factorial analysis was applied to identify the possible emission sources. Prior communalities were estimated by the squared multiple correlations with all other variables. We used the varimax rotation method and the criterion for factor selection was the number of eigenvalues greater than one. Results indicate that BC and NP were 4- and 3.2-times higher in urban outdoor caused by traffic emissions, respectively. Highest concentrations occurred during rush hours and during the pickup time of children. In urban school, BC was directly related to accumulation mode (N_49-205), while in the rural area, BC was related to local traffic and particles from pulp industries in the regional background. Nucleation mode (N_11-36) was related to traffic emissions in urban school, while in the rural school was related with secondary formation of particles. Mean I/O ratios of BC and NP in the urban (0.54; 0.51) and rural (0.71; 0.91) schools, respectively, suggested that their higher concentrations occurred in outdoors. VOCs were higher indoor in urban (I/O = 1.97) and rural (I/O = 2.22) sites, indicating these pollutants are generated inside, regardless of urban or rural sites. These findings suggest the necessity of improving ventilation and commuting styles to lower the exposure of children to air pollutants in and around school environments.

Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques

The complex mixtures of local emission sources and regional transportations of air pollutants make accurate PM2.5 prediction a very challenging yet crucial task, especially under high pollution conditions. A symbolic representation of spatio-temporal PM2.5 features is the key to effective air pollution regulatory plans that notify the public to take necessary precautions against air pollution. The self-organizing map (SOM) can cluster high-dimensional datasets to form a meaningful topological map. This study implements the SOM to effectively extract and clearly distinguish the spatio-temporal features of long-term regional PM2.5 concentrations in a visible two-dimensional topological map. The spatial distribution of the configured topological map spans the long-term datasets of 25 monitoring stations in northern Taiwan using the Kriging method, and the temporal behavior of PM2.5 concentrations at various time scales (i.e., yearly, seasonal, and hourly) are explored in detail. Finally, we establish a machine learning model to predict PM2.5 concentrations for high pollution events. The analytical results indicate that: (1) high population density and heavy traffic load correspond to high PM2.5 concentrations; (2) the change of seasons brings obvious effects on PM2.5 concentration variation; and (3) the key input variables of the prediction model identified by the Gamma Test can improve model's reliability and accuracy for multi-step-ahead PM2.5 prediction. The results demonstrated that machine learning techniques can skillfully summarize and visibly present the clusted spatio-temporal PM2.5 features as well as improve air quality prediction accuracy.

A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data

Air pollution in urban regions remains a crucial subject of study, given its implications on health and environment, where much effort is often put into monitoring pollutants and producing accurate trend estimates over time, employing expensive tools and sensors. In this work, we study the problem of air quality estimation in the urban area of Milan (IT), proposing different machine learning approaches that combine meteorological and transit-related features to produce affordable estimates without introducing sensor measurements into the computation. We investigated different configurations employing machine and deep learning models, namely a linear regressor, an Artificial Neural Network using Bayesian regularization, a Random Forest regressor and a Long Short Term Memory network. Our experiments show that affordable estimation results over the pollutants can be achieved even with simpler linear models, therefore suggesting that reasonably accurate Air Quality Index (AQI) measurements can be obtained without the need for expensive equipment.

Public Transport Strikes and Their Relationships With Air Pollution, Mortality, and Hospital Admissions

There is limited suggestive evidence of relationships between public transport strikes and either increased air pollution or worse population health. In this study we aimed to assess whether public transport strikes were associated with increases in health events (overall, cardiovascular and respiratory mortality, and cardiovascular and respiratory hospitalizations). We also explored whether air pollution mediated those associations. We used data from the city of Barcelona (Spain) for the period 2005-2016 on strikes, health events, and ambient air pollution (nitrogen dioxide, nitrogen monoxide, particulate matter (PM) with an aerodynamic diameter ≤10 μm, PM with an aerodynamic diameter ≤2.5μm, PM with an aerodynamic diameter ≤1μm, number of particles with a diameter greater than 5 nm per cm3 (particle number concentration), and black carbon). We used linear and quasi-Poisson regression models to explore the associations between air pollution and public transport strikes and between public transport strikes and health outcomes. We also investigated potential causal mediation by air pollution. Overall, this study suggested that public transport strikes are associated with increased overall mortality, respiratory mortality, and respiratory hospitalizations. However, our findings suggest that such increases are not mediated by the increase in air pollution. Our results indicate the need to further investigate these relationships and potential mechanisms.

A Study of the Socioeconomic Forces Driving Air Pollution Based on a DPSIR Model in Henan Province, China

The 3D spatiotemporal distribution (spatial and annual-month-daily temporal) features of the air quality index (AQI), air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h), and air pollution risks (R) from 2003 to 2018 were investigated to understand the severity of air pollution in Henan province. The multiyear ascending trends for AQI and R values, with a peak in 2015, were observed in 2003 to 2018 since the annual population-weighted average concentrations of PM2.5, PM10, NO2, and O3-8h were always higher than the CAAQs II from 2013 to 2017 in Henan province. Changes in the monthly moving average AQI values in Henan province conformed to a U-shaped pattern, with the highest values in the winter (from December to February) and the lowest in the summer (from June to August). Triple peaks for AQI values of 8:00‒10:00 a.m., 6:00‒8:00 p.m., and 2:00‒4:00 p.m. in the representative municipalities corresponded with the morning and evening traffic tendencies and photochemical process. A spatial analysis indicated that there were decreasing trends for air pollution from northwest to southeast in Henan province. Data on 48 concrete parameters were collected from 2003 to 2017 to construct a driving force‒pressure‒state‒influence‒response (DPSIR) model for assessing the socioeconomic forces driving air pollution in this province. It was the too-rapid growth of the driving force index (DFI), induced by urban development and population growth (UDPG), economic growth and change of industrial structure (EGCIS), and energy consumption growth and structure change (ECGSC), that led to a direct increase in the atmospheric pollution burden, i.e., total emissions from air pollution and industrial emissions, which are linearly correlated to values of UDPG and ECGSC, respectively (p < 0.05). Furthermore, the prediction models for AQI and R values in Henan province, with the growth rates being 4.251 DFI−1 and 0.0816 DFI−1, respectively, were simulated by multiple linear regression analysis. Therefore, the integrated risks of air pollution in Henan province were originally driven by DFI.

Transforming Our Cities: Best Practices Towards Clean Air and Active Transportation

PURPOSE OF REVIEW

By 2050, 70% of the global population will live in urban areas, exposing a greater number of people to specific city-related health risks that will only be exacerbated by climate change. Two prominent health risks are poor air quality and physical inactivity. We aim to review the literature and state the best practices for clean air and active transportation in urban areas.

RECENT FINDINGS

Cities have been targeting reductions in air pollution and physical inactivity to improve population health. Oslo, Paris, and Madrid plan on banning cars from their city centers to mitigate climate change, reduce vehicle emissions, and increase walking and cycling. Urban streets are being redesigned to accommodate and integrate various modes of transportation to ensure individuals can become actively mobile and healthy. Investments in pedestrian, cycling, and public transport infrastructure and services can both improve air quality and support active transportation. Emerging technologies like electric and autonomous vehicles are being developed and may reduce air pollution but have limited impact on physical activity. Green spaces too can mitigate air pollution and encourage physical activity. Clean air and active transportation overlap considerably as they are both functions of mobility. The best practices of clean air and active transportation have produced impressive results, which are improved when enacted simultaneously in integrated policy packages. Further research is needed in middle- and low-income countries, using measurements from real-world interventions, tracing air pollution back to the sources responsible, and holistically addressing the entire spectrum of exposures and health outcomes related to transportation.

Identifying the contributions of multiple driving forces to PM10-2.5 pollution in urban areas in China

Economic development and urban expansion have accelerated particulate matter pollution in urban areas in China. Particulate matter-driven haze poses a serious threat to human beings from a public health point of view. Substantial evidences had linked adverse health effects with exposures to PM_2.5, but recent research indicated that PM_10-2.5 also had great risk. However, the relative contributions of driving forces to PM_10-2.5 pollution are not well understood in the urban areas in China, and no targeted policies have been regulated to control the pollution. In this study, we quantified the contributions of potential driving factor across China with the structural equation model (SEM). Our results showed that in 2015 and 2016, the annual average concentrations of PM_10-2.5 in the 290 prefecture-level cities with a mean value of 36 and 35 μg/m³, respectively. Industrial scale contributed more to PM_10-2.5 pollution than city size and residents' activities in urban areas based on SEM results. Driving forces included in our model could explain 42% of variations in PM_10-2.5 pollution, which indicated that there existed influences from other anthropogenic sources and natural sources. Eleven targeted recommendations were then proposed to control PM_10-2.5 pollution based on our mechanism analysis. Findings from our study are beneficial to control PM_10-2.5 pollution on a national scale, and also can provide a theoretical basis for the formulation of PM_10-2.5 pollution control policy in China.

Fresh and Oxidized Emissions from In-Use Transit Buses Running on Diesel, Biodiesel, and CNG

The potential effect of changing to a nonfossil fuel vehicle fleet was investigated by measuring primary emissions (by extractive sampling of bus plumes) and secondary mass formation, using a Gothenburg Potential Aerosol Mass (Go:PAM) reactor, from 29 in-use transit buses. Regarding fresh emissions, diesel (DSL) buses without a diesel particulate filter (DPF) emitted the highest median mass of particles, whereas compressed natural gas (CNG) buses emitted the lowest (^MdEF_PM 514 and 11 mg kg_fuel^-1, respectively). Rapeseed methyl ester (RME) buses showed smaller ^MdEF_PM and particle sizes than DSL buses. DSL (no DPF) and hybrid-electric RME (RME_HEV) buses exhibited the highest particle numbers (^MdEF_PN 12 × 10¹⁴ # kg_fuel^-1). RME_HEV buses displayed a significant nucleation mode ( D_p< 20 nm). EF_PN of CNG buses spanned the highest to lowest values measured. Low ^MdEF_PN and ^MdEF_PM were observed for a DPF-equipped DSL bus. Secondary particle formation resulting from exhaust aging was generally important for all the buses (79% showed an average EF_PM:AGED/EF_PM:FRESH ratio >10) and fuel types tested, suggesting an important nonfuel dependent source. The results suggest that the potential for forming secondary mass should be considered in future fuel shifts, since the environmental impact is different when only considering the primary emissions.