Getting to the STEM of Environmental Justice: Equity Causes Enrich Student Learning in Science Classrooms

From satellites to ground-based sensors, as well as mobile networks of monitors, the availability of massive data sets has increased the need for educating students in data literacy in order to ensure their competency in the global market (Bluhm et al. 2020;Gibson and Mourad 2018). The U.S. Environmental Protection Agency (EPA) defines environmental justice as, "... the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income, with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies." According to Lacombe, more individuals die yearly from car exhaust (53,000) in the United States than road casualties (37,400). Students worked in groups to discuss their assumptions about factors they perceived to have an impact on air pollution levels (e.g., affluence, traffic, and vegetation).

The French automotive industry faces the challenge of the ecological transition

[...]the vehicle of the future must be considered within the broader framework of the mobility of the future, taking into account the entire value chain. Transforming our future into a sustainable future: planning for the automotive transition Didier Sepulchre de Condé, Mechanical industry The automotive industry is in turmoil, firstly because of the economic situation, with a market deeply affected by Covid, shortages of materials and components and disoriented customers;and secondly because of the structural situation, with the forced transition to electrification. The four paradoxes of the ecological transition of the European car industry Alois Kirchner, Former Director of Cabinet of the Minister for Industry The energy transition in the automotive sector is essential for achieving French and European climate objectives. [...]the actions implemented come up against four paradoxes, which must be overcome if this transition is to succeed: * the regulation on the reduction of CO2 emissions from the tank to the wheel, to the exclusion of other sources which now represent the majority of emissions from new vehicles;* the steering of vehicle traffic restrictions based on Crit'air stickers, leading to the prohibition of access to certain cities for vehicles that are more virtuous than others that are still allowed to enter;* the inability to implement policies to support the production of vehicles on European soil that are sufficiently powerful to halt the fall in associated jobs;a situation that benefits production sites that are not subject to the same environmental standards;* and the rising price of "green" vehicles, leading to a slowdown in the renewal of the fleet and the maintenance of a high level of pollution and carbon emissions.

Machine Learning Approaches Used for Air Quality Forecast: A Review

Air Quality Index (AQI) is an indicator of the pollution level of our surroundings and household. Prediction of the AQI values from the historical values can help us analyze and mitigate the pollution levels. The AQI values can be classified into predetermined categories and machine learning algorithms can be made use of to improve the classification accuracy of the Air Quality Index value calculated. The main objective of the paper is to provide the potential researchers, with the importance of various Machine Learning approaches used for the forecast of the Air Quality Index. This paper analyzes various strategies used for the prediction, classification of AQI incorporating machine learning techniques. The air quality index can be calculated using Machine learning-based methods. Some of the methods to be considered are logistic regression, decision tree, support vector regression, support vector classifier, random forest tree, Naive Bayes classifier, and K-nearest neighbor. Application of these methods on the Air Quality Index datasets may yield different Accuracy, Recall, and F1 Score. Different algorithms that can be used for the said purpose with their strengths are summarized in a comparison table.

Integral Assessment of Atmospheric Air Quality in the Largest Cities of Russia Based on TROPOMI (Sentinel-5P) Data for 2019–2020

This paper considers the level of atmospheric air pollution of the 20 largest cities in Russia in 2019–2020. The data used for the study is initially collected by a TROPOMI instrument (on the Sentinel-5P satellite), including measurements of carbon monoxide, formaldehyde, nitrogen dioxide, sulfur dioxide, and aerosol (aerosol index). The measurements were obtained using the cloud-based platform, Google Earth Engine, which presents L3 level data available for direct analysis. The Tropomi Air Quality Index (TAQI) integrates available TROPOMI measurements into a single indicator. The calculation results showed that most of the cities under consideration (15 out of 20) have a low or higher than usual level of pollution. Formaldehyde (35.7%) and nitrogen dioxide (26.4%) play the main role in the composition of pollution particles. A significant share is occupied by sulfur dioxide (16.4%). The contribution of carbon monoxide and aerosol averages 10.8 and 10.6%, respectively. Air pollution in cities is caused by both natural (wildfires, dust storms) and anthropogenic (seasonal migrations of the population, restrictions due to the COVID-19 pandemic) factors. Estimating atmospheric pollution levels in urban areas using an integral index based on remote data (such as TAQI) can be considered as a valuable information addition to existing ground-based measuring systems within the multisensory paradigm.

Effect of COVID-19 pandemic restrictions on air pollution at a local scale in urban areas affected by high-intensity vehicle traffic in Poland

The lockdown in 2020 implemented due to the SARS-CoV-2 pandemic has resulted in a significant improvement in air quality at a global scale. Nationwide lockdown also considerably improved air quality at a local scale, especially in cities which were almost completely shut down during the first coronavirus wave, with nearly no activity. We tested the hypothesis that a reduction in the intensity of vehicle traffic causes a drastic decrease in urban air pollution at a local scale. We focused on two urban agglomerations, Warsaw and Cracow, in Poland. Data of the concentrations of traffic-related sources, namely NOx, PM10, and PM2.5, obtained from two air pollution monitoring stations were analyzed for the years 2020 and 2021, during which lockdown and pandemic restrictions were in effect, and for 2019, as a reference. In the years 2020–2021, the average annual concentration of NOx was decreased by ~ 19%, PM2.5 by ~ 19%, and PM10 by ~ 18% in Warsaw, while in Cracow the average annual concentration of NOx was decreased by ~ 16%, PM2.5 by ~ 22%, and PM10 by ~ 2%, compared to 2019. The contribution from traffic-related sources to the overall level of air pollution was estimated. The results indicated that ~ 30 µg/m3 of PM10, ~ 15 µg/m3 of PM2.5, and ~ 120 µg/m3 of NOx in Cracow, and ~ 20 µg/m3 of PM2.5 in Warsaw originate from moving vehicles. The nationwide lockdown allowed us to conduct this study to understand how a reduction in local traffic emissions can decrease ambient air pollution levels.

Statistical assessment of COVID-19 lockdowns on ambient air quality, Himachal Pradesh and learnings for implementing clean technologies: insight from industrial town, India

PurposeThe current study investigates the impact of the coronavirus disease 2019 (COVID-19) lockdown restrictions on air quality in an industrial town in Himachal Pradesh (HP) (India) and recommends policies and strategies for mitigating air pollution.Design/methodology/approachThe air quality parameters under study are particulate matter10 (PM10), PM2.5, SO2 and NO2. One-way ANOVA with post-hoc analysis and non-parametric Kruskal–Wallis test, and multiple linear regression analysis are used to validate the data analysis results.FindingsThe findings indicate that the lockdown and post-lockdown periods affected pollutant levels even after considering the meteorological conditions. Except for SO2, all other air quality parameters dropped significantly throughout the lockdown period. Further, the industrial and transportation sectors are the primary sources of air pollution in Paonta Sahib.Research limitations/implicationsFuture research should include other industrial locations in the state to understand the relationship between regional air pollution levels and climate change. The findings of this study may add to the discussion on the role of adopting clean technologies and also provide directions for future research on improving air quality in the emerging industrial towns in India.Originality/valueVery few studies have examined how the pandemic-induced lockdowns impacted air pollution levels in emerging industrial towns in India while also considering the confounding meteorological factors.Graphical abstract

Lockdown Effects on Air Quality in Megacities During the First and Second Waves of COVID-19 Pandemic

Air pollution is among the highest contributors to mortality worldwide, especially in urban areas. During spring 2020, many countries enacted social distancing measures in order to slow down the ongoing COVID-19 pandemic. A particularly drastic measure, the "lockdown”, urged people to stay at home and thereby prevent new COVID-19 infections during the first (2020) and second wave (2021) of the pandemic. In turn, it also reduced traffic and industrial activities. But how much did these lockdown measures improve air quality in large cities, and are there differences in how air quality was affected? Here, we analyse data from two megacities: London as an example for Europe and Delhi as an example for Asia. We consider data during first and second-wave lockdowns and compare them to 2019 values. Overall, we find a reduction in almost all air pollutants with intriguing differences between the two cities except Delhi in 2021. In London, despite smaller average concentrations, we still observe high-pollutant states and an increased tendency towards extreme events (a higher kurtosis of the probability density during lockdown) during 2020 and low pollution levels during 2021. For Delhi, we observe a much stronger decrease in pollution concentrations, including high pollution states during 2020 and higher pollution levels in 2021. These results could help to design policies to improve long-term air quality in megacities.

Lockdown as an Empirical Strategy to Curb Air Pollution: Evidence from a Quasi‐Natural Experiment

In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID‐19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM10 (27%), PM2.5 (19%), NO2 (74%), SO2 (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21‐days lockdown is implemented, then a reduction of 42 µg m−3 in PM10, 23 µg m−3 in PM2.5, 14 µg m−3 in NO2, 2 µg m−3 in SO2, and 0.7 mg m−3 in CO can be achieved.

Paleofire Data for Public Health Nursing Wildfire Planning: A Planetary Perspective

Public health Is Increasingly threatened by global warming, land use, and changing wildfire patterns that shape vegetation type, structure, and biodiversity and ultimately affect ecosystem services and our society.1 Uncontrolled large wildfires emit greenhouse gases and aerosols that induce direct and indirect climate feedback through radiative forcing in the atmosphere2 and irreversible changes of natural vegetation, thereby further accelerating climate change and associated fire risks.3 Wildfires are also harmful to human health because they create high pollution concentrations of fine particulate matter that are 2.5 micrometers or smaller (PM2.5) and concentrations of coarse particulate matter that are between 2.5 and 10 micrometers in size. When inhaled, particulate matter significantly increases a myriad of health outcomes, including overall mortality, cardiovascular mortality, and emergency department visits for respiratory morbidity, congestive heart failure, chronic obstructive pulmonary disease, and angina.4,5 Between July and October 2020, high PM2.5 concentrations from massive wildfires surrounding a large regional hospital in the western United States were associated with a 6% increase in COVID-19 cases.6 Risks for developing adverse health effects from wildfire smoke are greatest among people who are living with chronic conditions;who are experiencing intergenerational racial, economic, and housing discrimination;and who are facing social inequities from the COVID-19 pandemic.4The unprecedented recent wildfires in the western United States and their ill effects on human health and society, as well as the multiple other threats to people and places brought about by climate change, draw attention to the increasing urgency of developing new public health approaches and long-term adaptation strategies to support future population health. Observational fire data covering the past few decades give valuable information on current wildfire events.1 However, these data hardly capture long-term trends (i.e., centennial to millennial time scales) of wildfires and associated atmospheric emissions that may help to improve future fire models and thereby provide the base to adapt public health systems.3 To understand long-term trends, natural archives preserve fire history on a wide range ofspatial scales in the past beyond the period of observational fire data;examples include polar and highalpine ice cores;lake, peat, and marine sediment cores.3,8,9 Such paleofire records are based on measurements of the gaseous tracers ammonium and nitrate or particulate matter, such as levoglucosan and black carbon, and charcoal that reflect different components of wildfire-induced atmospheric smoke pollution.8,9 These paleofire records have previously identified complex regional interactions of humans, ecosystems, and climate change.3 Submicron-sized (100-500 nm in diameter) black carbon particles from wildfires and fossil fuel during the industrial era (i.e., the past 250 years) measured in ice cores and lake sediments can be used as a direct tracer for the release of harmful PM2.5 to the atmosphere.8,10 Such paleo black carbon records have been established from both polar and high-alpine glaciers on several continents and are recently developed from lake sediments.10 These found significant changes of fire activity in response to climate and human impact and enhanced pollution levels varying both in time and space. With public health nurses being well positioned to understand population health needs, planetary health, and the health consequences of wildfires, public health nurses can improve upon wildfire adaptation planning and essential public health services by understanding historical perspectives from past fires.9,11,13 Paleofire data provide direct estimates of historical atmospheric emissions from past wildfires and associated harmful concentrations of particulate matter over long distances.

Impacts of Air Pollution and Dampness on Occupant Respiratory Health in Unplanned Houses: A Case Study of Bandung, Indonesia

This paper presents the results from field measurements and household surveys on the severity of indoor mold risk and its impact on respiratory health in a typical unplanned neighborhood of kampungs in Bandung, Indonesia. Mold risk was investigated using fungal risk detectors (n = 102), while air pollution levels were established with total suspended particulate (TSP) and particulate matter (PM2.5) (n = 38). The self-reported prevalence of respiratory diseases was obtained using a questionnaire form (ATS-DLD-78) (n = 599). The results showed that respiratory health problems were higher in the rainy season, particularly among children. Most houses suffered from severe mold risk, primarily due to extreme humid weather conditions, especially during rainy season (97%) where water leakage was prevalent (60%). In addition, the TSP and PM2.5 concentrations exceeded the WHO standards in most kampung houses, where around 58% of the houses recorded higher outdoor mean PM2.5 concentrations than indoors. Further, the path analysis showed that allergies followed by humidity rate and smell, which were affected by window-opening duration, directly impacted children’s respiratory health. Smoking behavior and building-related health problems, due to exposure to outdoor air pollution, affected the respiratory health of those aged 15 years old and over.

Delhi’s poor return to dirty fuel in COVID-19 lockdown

Speed read Some five million people were using unclean cooking fuels during COVID-19 lockdown Urban poverty worse than in India’s rural areas because of cramped living conditions Without subsidies millions cannot afford to use cooking gas in Delhi [NEW DELHI] COVID-19 lockdowns exposed many truths but none so stark as the poverty that exists in the Indian capital of New Delhi as desperate urban poor families were compelled to switch to wood and dung to keep home fires going. A study released this month by the environmental NGO Chintan suggests that urban poverty may be worse than India’s rural areas because of unhygienic living conditions, including high indoor pollution levels generated by burning biomass in confined living spaces. According to the study 36 per cent of low-income housing groups in New Delhi rely on ‘unclean’ sources of fuel for their cooking needs.

PM10 variation, composition, and source analysis in Tuscany (Italy) following the COVID-19 lockdown restrictions

To control the spread of COVID-19, exceptional restrictive measures were taken in March 2020 that imposed a radical change on the lifestyle of millions of citizens around the world, albeit for a short period. The national lockdown, which lasted from 10 March to 18 May 2020 in Italy, was a unique opportunity to observe the variation in air quality in urban environments under conditions comprising almost total traffic restriction and a strong reduction in work activities. In this paper, the data from 17 urban monitoring sites in Tuscany are presented, and the PM and NO2 concentrations in the 2 months before the start of the lockdown and the 2 months after lockdown are compared with the corresponding months of the previous 3 years. The results show that the total loads of PM2.5 and PM10 (particulate matter with an aerodynamic diameter smaller than 2.5 and 10 µm, respectively) decreased, but they did not exhibit significant changes compared to previous years, whereas NO2 underwent a drastic reduction. For three of these sites, the chemical composition of the collected samples was measured using thermal–optical techniques, ion chromatography, and particle-induced X-ray emission analysis, and the application of multivariate positive matrix factorization analysis also allowed for PM10 source identification and apportionment. Using these analyses, it was possible to explain the low sensitivity of PM10 to the lockdown effects as being due to different, sometimes inverse, behaviors of the different sources that contribute to PM. The results clearly indicate a decline in pollution levels related to urban traffic and an increase in the concentration of sulfate for all sites during the lockdown period.

The dynamics of the first wave of COVID-19 on environment and wildlife– a boon or a bane?

Even though COVID-19 has drastically weighed upon the humankind, still there is a "silver lining" to see in this dark time. Amidst of this pandemic, most of the human activities were restricted to break the chain of infection which resulted the remarkable change in nature. It has been reported that due to halt in air travel, reduction in the use of fossil fuels, way less functioning of vehicles, shutdown of industries has complied the change in air pollution levels and also change in river water quality. Reports also showed the reduction in particulate matter (PM 2.5 and PM 10), greenhouse gases emissions, massive improvement in the Air quality index (AQI), reduction in the NOX and SOX's levelhas clearly stipulated that nature has got it's time to "revive". Even the global carbon emission has reported to reduced reluctantly which is expected to be the biggest such drop since World War II. Despite conducting water-cleansing projects and spending a lot of money, the situation of the water bodies were far better now during first lockdown. Moreover, migration and breeding of the birds and animals have been reported to be restored to normal pattern due to depletion in man-animal conflict. Apart from the positive, negative impacts on the nature are also being experienced. Our review work is highlighting such impacts witnessed during the first wave of COVID-19, like, the significant improvement in air and water quality, reduction in environmental noise, therefore an in turn cleaner and quieter habitat for the wildlife to mate and also to quench their curiosities by their surprising excursions;but there are also some negative aspects as well, like reduction in recycling and the increase in waste, increased poaching and even lone shuttering of zoo animals.

Modeling Surface Air Pollution with Reduced Emissions during the COVID-19 Pandemic Using CHIMERE and COSMO-ART Chemical Transport Models

The results of numerical modeling of air pollution using CHIMERE and COSMO-ART chemical transport models are presented. The modeling was performed according to the scenarios of the 50–60% reduction of emissions from anthropogenic sources in the Moscow region during the period of March–July 2020. Scenario calculations of pollutant concentrations were compared with baseline simulations using regionally adapted inventory of anthropogenic pollutant emissions to the atmosphere. The most significant decrease in the concentrations of NO2 and CO was reproduced by the models when emissions from two sectoral sources (vehicles and nonindustrial plants) were reduced. The PM10 drop was mostly influenced by the reduction of emissions from industrial combustion. With the total reduction of emissions from anthropogenic sources as compared to the baseline calculations, the pollutant concentration decreased by 44–54% for NO2, by 38–44% for CO, and by 26–39% for PM10. This generally coincides with the quantitative estimates of the pollution level drop obtained by other authors. The greatest effect of reducing pollutant emissions into the atmosphere was found during the episodes of adverse weather conditions for air purification, when the simulated and observed pollution level increases by 3–5 times as compared to the conditions of intense pollutant dispersion.

Stereoscopic Monitoring: A Promising Strategy to Advance Diagnostic and Prediction of Air Pollution

Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions;2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles;and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.

The impact of air pollutant emissions during the COVID-19 pandemic on Air quality in China

The outbreak of COVID-19 has led to the adoption of a series of strict epidemic prevention and control measures across the country, which has had a significant impact on people’s production and life patterns. It also provides an opportunity to explore the relationship between air quality and human social and economic activities. Based on the National Urban Air Quality Report from February 2019 to February 2021 and passenger transport capacity data from February 2019 to February 2021, the regression analysis was conducted to understand how human factors related to pollution sources affect ambient air quality levels. The study showed that passenger volume and air pollution levels decreased significantly during the epidemic period. Human factors had no significant impact on O3 concentration. However, they had a significant impact on PM, SO2, NO2, and CO. Finally, explore how to improve air quality from the Angle of human factors and build a low-carbon economy.

Geospatial View of Air Pollution and Health Risk Over North Indian Region in COVID-19 Scenario

Air pollutant concentration, air quality index (AQI), and Excess risk (ER%) is assessed during January 2020 to June 2021 and in three scenarios including pre-lockdown, lockdown and post-lockdown based on 47 ground station data (during January 2020 to June 2020) distributed over northern part of India (including Delhi, Haryana, Punjab, part of Uttar Pradesh, and part of Rajasthan) using statistics and geographic information system (GIS) techniques. Daily and monthly variations of air pollutants (During January 2020 to June 2021) over the region showed a systematic pattern with high pollutant level during October and November while low during March, April (in dry period) and July–September (in wet period). In three scenarios viz. pre, during and post-lockdown the average concentration for PM2.5 was 71.1 ± 45 µg/m3, 39 ± 20 µg/m3 and 40 + 17 µg/m3, for PM10 was 139 ± 72 µg/m3, 96 ± 55 µg/m3 and 105 ± 57 µg/m3, for NO2 was 28 ± 21 µg/m3, 17 ± 13 µg/m3 and 18 ± 12 µg/m3, for NH3 was 33 ± 24 µg/m3, 25 ± 18 µg/m3 and 29 ± 22 µg/m3, for CO was 1 ± 0.65 mg/m3, 0.7 ± 0.5 mg/m3, and 0.7 ± 0.5 mg/m3, for O3 was 29 ± 20 µg/m3, 39 ± 23 µg/m3 and 39 ± 22 µg/m3 and for SO2 was 14 ± 11 µg/m3, 14 ± 12 µg/m3 and 12.5 ± 8.9 µg/m3. Significant decrease in mean pollutants concentration, AQI and ER % was observed in lockdown period amid COVID-19. PM2.5, PM10, NO2, NH3 and CO decreased by 46%, 31%, 39%, 24% and 34%, respectively, in lockdown scenario as compared to the pre-lockdown scenario while the O3 get increased. A decrease of 39% in AQI was observed as compared to pre-lockdown scenario;however, the difference was less when compared with post-lockdown scenario. The decrease in total ER% was 60.36% over the study area due to improvement in air quality over the region amid COVID-19 lockdown. The meteorological conditions in 2020 were found consistent with respect to 2019 and very less influence was observed on the concentration of air pollutants (less r2 among the pollutants and meteorological parameters).

Air Quality Status During Pandemic Covid19 in Urban and Sub-Urban Area in Malaysia

This paper discussed the API status in the urban and sub-urban cities in Malaysia before, during and after Movement Control Order (MCO) was implemented in year the 2020. The lockdown implemented worldwide including Malaysia has come as a respite to the environment, especially in air pollutant levels. 2 cities have been chosen in this study that is Kangar (suburban) and Cheras (urban). The Air Pollutant Index (API) data recorded on an hourly basis throughout different series of national lockdown phases was obtained from the Malaysian Air Pollutant Index website. The findings show that the API status quality in sub-urban does not change much compare to the urban area. But it can be seen that the API reduces during MCO implementation in the sub-urban and urban areas. This is because, during the MCO, there are several restrictions on mass movement and gatherings, and the closure of educational institutions, government and private agencies (except for essential services). This MCO restriction reduced the traffic density, industrial activities, and other social activities.

Air quality status before, during and after the pandemic COVID-19 Movement Control Order (MCO) at urban and suburban areas in Malaysia

A severe acute respiratory syndrome, COVID-19 outbreak started in December 2019 reported in the city of Wuhan, China has spread rapidly in other countries around the world. Declared as a pandemic, the lockdowns, industrial shutdowns, closing international borders and overall reduced mobility of the population either for tourism or work commutes have led to air pollution reduction. The lockdown implemented worldwide including Malaysia has come as a respite to the environment, especially in air pollutant levels. Utilizing the Air Pollutant Index (API) data recorded on an hourly basis throughout different series of national lockdown phases, this study is undertaken to evaluate the air quality status before, during and after the implementation of MCO by comparing the selected urban and suburban areas in Malaysia. Six (6) locations including Bandaraya Melaka, Cheras and Alor Setar (urban) and Seri Manjung, Kangar and Kuantan (suburban) were chosen in this study. The result revealed that the air quality has shown improvements after a further extension of lockdown days at both urban (API 36.3-61.5%) and suburban areas (API 46.5-48.6%). It has indirectly changed the air quality status from ‘moderate’ to ‘good’ in all 6 urban and suburban areas. The air quality trends, however, gradually deteriorate and has increased as more restrictions were eased at a later stage. The implementation of lockdown measures not only restricted the spread of infection rate but has reduced ambient air pollutants levels that lead to enhanced air quality. The results obtained in this study can be a useful insight for the regulatory agencies in controlling air pollution and restore the environment quality.

A Survey of Technologies and Recent Developments for Sustainable Smart Cycling

Among the problems resulted from the continuous urbanization process, inefficient urban mobility and high pollution levels have been complex challenges that have demanded a lot of public investments and research efforts. Recently, some alternative transportation means have been leveraged as sustainable options for such challenges, which has brought bicycles to a more relevant setting. Besides the sometimes obvious benefits of adopting bikes for transportation, technologies around the Internet of Things (IoT) paradigm have been advocated as important supportive tools to boost smart cycling initiatives. Actually, new technologies can be exploited to improve the efficiency of bike paths and parking spots, while reducing accidents and enhancing the cycling experience of the users. Therefore, in this highly vibrating scenario, this article facilitates the understating of current research trends and promising developments, surveying and classing recent works. Since there is a global interest for the promotion of cleaner and more sustainable solutions in large cities, this survey can be valuable when supporting new developments in this highly relevant research area.