A new approach for inferring traffic-related air pollution: Use of radar-calibrated crowd-sourced traffic data

Spike in pollution to ignite the bursting of COVID-19 second wave is more dangerous than spike of SAR-CoV-2 under environmental ignorance in long term: a review

Specific areas in many countries such as Italy, India, China, Brazil, Germany and the USA have witnessed that air pollution increases the risk of COVID-19 severity as particulate matters transmit the virus SARS-CoV-2 and causes high expression of ACE2, the receptor for spike protein of the virus, especially under exposure to NO₂, SO₂ and NOx emissions. Wastewater-based epidemiology of COVID-19 is also noticed in many countries such as the Netherlands, the USA, Paris, France, Australia, Spain, Italy, Switzerland China, India and Hungary. Soil is also found to be contaminated by the RNA of SARS-CoV-2. Activities including defecation and urination by infected people contribute to the source for soil contamination, while release of wastewater containing cough, urine and stool of infected people from hospitals and home isolation contributes to the source of SARS-CoV-2 RNA in both water and soil. Detection of the virus early before the outbreak of the disease supports this fact. Based on this information, spike in pollution is found to be more dangerous in long-term than the spike protein of SARS-CoV-2. It is because the later one may be controlled in future within months or few years by vaccination and with specific drugs, but the former one provides base for many diseases including the current and any future pandemics. Although such predictions and the positive effects of SARS-CoV-2 on environment was already forecasted after the first wave of COVID-19, the learnt lesson as spotlight was not considered as one of the measures for which 2nd wave has quickly hit the world.

Assessing Neighborhood-scale Traffic from Crowd-sensed Traffic Data: Findings from an Environmental Justice Community in New York City

BACKGROUND

The waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse.

OBJECTIVE

The goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 to 2019.

METHODS

We analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion.

RESULTS

In the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night.

CONCLUSIONS

Neighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures.

Family, neighborhood and psychosocial environmental factors and their associations with asthma in Australia: a systematic review and Meta-analysis

OBJECTIVE

Various associations between different environmental exposures and asthma have been reported in different countries and populations. We aimed to investigate the associations between family, neighborhood and psychosocial environmental factors and asthma-symptoms in Australia by conducting a systematic review and meta-analysis.

DATA SOURCES

We analyzed the primary research studies conducted in Australia across multiple databases, including PubMed, EMBASE and Scopus, published between 2000 and 2020.

STUDY SELECTIONS

The reviews and analyses focused on the overall association of different environmental exposures with the exacerbation of asthma-symptoms or asthma-related hospital visits. Quality-effect meta-analysis was done to estimate the pooled odds ratio for different environmental exposures for asthma-symptoms.

RESULTS

Among the 4799 unique published articles found, 46 were included here for systematic review and 28 for meta-analysis. Our review found that psychosocial factors, including low socioeconomic condition, maternal depression, mental stress, ethnicity, and discrimination, are associated with asthma-symptoms. Pooled analysis was conducted on family and neighborhood environmental factors and revealed that environmental tobacco smoking (ETS) (OR 1·69, 95% CI 1·19-2·38), synthetic bedding (OR 1·91, 95% CI 1·48-2·47) and gas heaters (OR 1·40, 95% CI 1·12-1·76) had significant overall associations with asthma-symptoms in Australia.

CONCLUSION

Although the studies were heterogeneous, both systematic review and meta-analysis found several psychosocial and family environmental exposures significantly associated with asthma-symptoms. Further study to identify their causal relationship and modification may reduce asthma-symptoms in the Australian population.

Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data

INTRODUCTION

To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.

METHODS

We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.

RESULTS

Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.

CONCLUSION

Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

Coupled models using radar network database to assess vehicular emissions in current and future scenarios

Vehicles are one of the most significant sources of air pollutant emissions in urban areas, and their real contribution always needs to be updated to predict impacts on air quality. Radar databases and traffic counts using statistical modeling is an alternative and low-cost approach to produce traffic activities data in each urban street to be used as input to predict vehicular emissions. In this work, we carried out a spatial statistical analysis of local radar data and calculated traffic flow using local radar data combined with different statistical models. Future scenarios about vehicle emission inventory to define public policies were also proposed and analyzed for Belo Horizonte (BH), a Brazilian State capital, with the third-largest metropolitan region in the country. The Normal-Neighborhood Model (i.e., the mixed effect model with random effect in the neighborhood, radar type, and in the regional area) was used to calculate traffic flow in each urban street. Results showed average reductions in CO (4.5%), NMHC (3.0%), NOx (3.0%) and PM_2.5 (6.2%) emissions even with an increase in fleet composition (25% in average). The decrease is a result of the implementation of emission control programs by the government, improvements vehicles technologies, and the quality of fuels. Prediction of traffic data from radar databases has proven to be useful for avoiding the high costs of performing origin-destination surveys and traffic modeling using commercial software. Radar databases can provide many potential benefits for research and analysis in environmental and transportation planning. These findings can be incorporated in future investigations to implement public policies on vehicular emission reduction in urban areas and to advance environmental health effects research and human health risk assessment.

Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data

INTRODUCTION

To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and results in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.

METHODS

We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.

RESULTS

Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.

CONCLUSION

Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

Opening a Large Delivery Service Warehouse in the South Bronx: Impacts on Traffic, Air Pollution, and Noise

Mott Haven, a low-income neighborhood in New York City, suffers from increased air pollution and accommodates several industrial facilities and interstates. In 2018, a large delivery service warehouse opened. Our objectives are to characterize black carbon (BC), fine particulate matter (PM_2.5), and noise in the community; model changes in traffic due to the facility opening; and estimate associated BC and noise changes. BC, PM_2.5, and noise were measured at eight sites pre-opening, and traffic counted continuously at two sites (June 2017–May 2019). An interrupted time series model was used to determine facility-related changes in traffic. Post-opening changes in traffic-related BC/noise were estimated from regressions of BC/noise with traffic flow. Mean (SD) pre-warehouse measures of BC and PM_2.5 were 1.33 µg/m³ (0.41) and 7.88 µg/m³ (1.24), respectively. At four sites, equivalent sound levels exceeded the EPA’s recommended 70 dBA limit. After the warehouse opening, traffic increased significantly, predominantly at night. At one site, the greatest change for trucks occurred 9PM-12AM: 31.7% (95%CI [23.4%, 40.6%]). Increased traffic translated into mean predicted increases of 0.003 µg/m³ (BC) and 0.06 dBA (noise). Though small, they negate the substantial decrease the community seeks. Our findings can help communities and policymakers better understand impacts of traffic-intensive facilities.

A Traffic-Based Method to Predict and Map Urban Air Quality

As global urbanization, industrialization, and motorization keep worsening air quality, a continuous rise in health problems is projected. Limited spatial resolution of the information on air quality inhibits full comprehension of urban population exposure. Therefore, we propose a method to predict urban air pollution from traffic by extracting data from Web-based applications (Google Traffic). We apply a machine learning approach by training a decision tree algorithm (C4.8) to predict the concentration of PM2.5 during the morning pollution peak from: (i) an interpolation (inverse distance weighting) of the value registered at the monitoring stations, (ii) traffic flow, and (iii) traffic flow + time of the day. The results show that the prediction from traffic outperforms the one provided by the monitoring network (average of 65.5% for the former vs. 57% for the latter). Adding the time of day increases the accuracy by an average of 6.5%. Considering the good accuracy on different days, the proposed method seems to be robust enough to create general models able to predict air pollution from traffic conditions. This affordable method, although beneficial for any city, is particularly relevant for low-income countries, because it offers an economically sustainable technique to address air quality issues faced by the developing world.

Urban health: an example of a "health in all policies" approach in the context of SDGs implementation

BACKGROUND

Cities are an important driving force to implement the Sustainable Development Goals (SDGs) and the New Urban Agenda. The SDGs provide an operational framework to consider urbanization globally, while providing local mechanisms for action and careful attention to closing the gaps in the distribution of health gains. While health and well-being are explicitly addressed in SDG 3, health is also present as a pre condition of SDG 11, that aims at inclusive, safe, resilient and sustainable cities. Health in All Policies (HiAP) is an approach to public policy across sectors that systematically takes into account the health implications of decisions, seeks synergies, and avoids harmful health impacts in order to improve population health and health equity. HiAP is key for local decision-making processes in the context of urban policies to promote public health interventions aimed at achieving SDG targets. HiAPs relies heavily on the use of scientific evidence and evaluation tools, such as health impact assessments (HIAs). HIAs may include city-level quantitative burden of disease, health economic assessments, and citizen and other stakeholders' involvement to inform the integration of health recommendations in urban policies. The Barcelona Institute for Global Health (ISGlobal)'s Urban Planning, Environment and Health Initiative provides an example of a successful model of translating scientific evidence into policy and practice with regards to sustainable and healthy urban development. The experiences collected through ISGlobal's participation implementing HIAs in several cities worldwide as a way to promote HiAP are the basis for this analysis.

AIM

The aim of this article is threefold: to understand the links between social determinants of health, environmental exposures, behaviour, health outcomes and urban policies within the SDGs, following a HiAP rationale; to review and analyze the key elements of a HiAP approach as an accelerator of the SDGs in the context of urban and transport planning; and to describe lessons learnt from practical implementation of HIAs in cities across Europe, Africa and Latin-America.

METHODS

We create a comprehensive, urban health related SDGs conceptual framework, by linking already described urban health dimensions to existing SDGs, targets and indicators. We discuss, taking into account the necessary conditions and steps to conduct HiAP, the main barriers and opportunities within the SDGs framework. We conclude by reviewing HIAs in a number of cities worldwide (based on the experiences collected by co-authors of this publication), including city-level quantitative burden of disease and health economic assessments, as practical tools to inform the integration of health recommendations in urban policies.

RESULTS

A conceptual framework linking SDGs and urban and transportplanning, environmental exposures, behaviour and health outcomes, following a HiAP rationale, is designed. We found at least 38 SDG targets relevant to urban health, corresponding to 15 SDGs, while 4 important aspects contained in our proposed framework were not present in the SDGs (physical activity, noise, quality of life or social capital). Thus, a more comprehensive HiAP vision within the SDGs could be beneficial. Our analysis confirmed that the SDGs framework provides an opportunity to formulate and implement policies with a HiAP approach. Three important aspects are highlighted: 1) the importance of the intersectoral work and health equity as a cross-cutting issue in sustainable development endeavors; 2) policy coherence, health governance, and stakeholders' participation as key issues; and 3) the need for high quality data. HIAs are a practical tool to implement HiAP. Opportunities and barriers related to the political, legal and health governance context, the capacity to inform policies in other sectors, the involvement of different stakeholders, and the availability of quality data are discussed based on our experience. Quantitative assessments can provide powerful data such as: estimates of annual preventable morbidity and disability-adjusted life-years (DALYs) under compliance with international exposure recommendations for physical activity, exposure to air pollution, noise, heat, and access to green spaces; the associated economic impacts in health care costs per year; and the number of preventable premature deaths when improvements in urban and transport planning are implemented. This information has been used to support the design of policies that promote cycling, walking, public, zero and low-emitting modes of transport, and the provision of urban greening or healthy public open spaces in Barcelona (e.g. Urban Mobility, Green Infrastructure and Biodiversity Plans, or the Superblocks's model), the Bus Rapid Transit and Open Streets initiatives in several Latin American cities or targeted SDGs assessments in Morocco.

CONCLUSIONS

By applying tools such as HIA, HiAP can be implemented to inform and improve transport and urban planning to achieve the 2030 SDG Agenda. Such a framework could be potentially used in cities worldwide, including those of less developed regions or countries. Data availability, taking into account equity issues, strenghtening the communication between experts, decision makers and citizens, and the involvement of all major stakeholders are crucial elements for the HiAP approach to translate knowledge into SDG implementation.