Real-time air pollution monitoring with sensors on city bus

This paper presents an experimental study on real-time air pollution monitoring using wireless sensors on public transport vehicles.The study is part of the GreenIoT project in Sweden,which utilizes Internet-of-Things to measure air pollution level in the city center of Uppsala.Through deploying low-cost wireless sensors,it is possible to obtain more fine-grained and real-time air pollution levels at different locations.The sensors on public transport vehicles complement the readings from stationary sensors and the only ground level monitoring station in Uppsala.The paper describes the deployment of wireless sensors on Uppsala buses and the integration of the mobile sensor network with the GreenIoT testbed.Extensive experiments have been conducted to evaluate the communication quality and data quality of the system.

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye infections.Air pollution also poses serious issues to the planet.Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions.Thus,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions.The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks.Localization is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not optimal.This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities.These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization.The dataset is divided into training and testing parts based on 10 cross-validations.The evaluation on predicting the air pollutant for localization is performed with the training dataset.Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

Air Pollution in the Gulf of Mexico

An integral analysis of Air Pollution in the Gulf of Mexico was made considering pollutants emissions assessment and diagnosis;air pollution monitoring;and modeling of air pollution dispersion. Combustion sources considered in this work were: thermoelectric power plants and open flares;and pollutants considered were sulfur dioxide, nitrogen dioxides, particulate matter (PM10 and PM2.5), Total suspended particles (TSP) and carbon monoxide (CO). This study made evident a lack of more recent information and a homogenization in emissions factors in order to know the conditions of air pollution in the Gulf of Mexico in a more reliable way.

Emerging challenges of ozone impacts on asian plants:actions are needed to protect ecosystem health

Context:Ozone concentrations near the land surface are rising in Asia while they are declining or stagnating in Europe and North America.Ozone is the most widespread air pollutant negatively affecting vegetation,and its increased concentrations pose a major threat to food quality and production and other ecosystem services in Asia.Method:In this review,we provide an overview of scientific challenges in the impacts of ozone pollution on Asian vegetation,and synthesize the challenges toward mitigation of the impacts.Result:We argue that new policy initiatives need to seek both reduction of ozone levels and enhancement of plant tolerance to ozone to maintain food quality and ensure food supplies.Conclusion:The scientific advancements must be transferred to actions by two types of institutions:a)environmental agencies for reducing ozone levels and b)agricultural research institutions for enhancing plant tolerance to ozone.In connecting the scientific advancements with the institutional actions,scientists in Asian countries should play the key role taking advantages of interdisciplinary and international collaborations.

Variations in traffic-related polycyclic aromatic hydrocarbons in PM2.5 in Kanazawa, Japan, after the implementation of a new vehicle emission regulation

A three-year sampling campaign was conducted at a roadside air pollution monitoring station in the urban area of Kanazawa, Japan. Due to a new emission regulation, PAHs levels decreased over the sampling campaign, exhibiting values of 706 ± 413 pg/m^(3) in 2017, 559 ±384 pg/m^(3) in 2018, and 473 ± 234 pg/m^(3) in 2019. In each year, similar seasonal variations in PAHs levels were observed, with higher levels observed in winter and lower levels in summer. Among the PAHs isomer ratios, we observed that the ratio of benzo[b]fluoranthene(BbF) and benzo[k]fluoranthene(BkF), [Bb F]/([BbF] + [BkF]), and the ratio of indeno[1,2,3-cd]pyrene(IDP) and benzo[ghi]perylene(BgPe), [IDP]/([BgPe] + [IDP]), showed stability over the sampling campaign and were less affected by the new emission regulation, seasonal variations, and regional characteristics. When using the combined ratio ranges of 0.66-0.80([Bb F]/[BbF] + [BkF]) and 0.26-0.49([IDP]/[Bg Pe] + [IDP]), traffic emissions were clearly distinguished from other PAHs emission sources. Principal component analysis(PCA) and positive matrix factorization(PMF) were also performed to further analyse the characteristics of traffic-related PAHs. Overall, this study affirmed the effectiveness of the new emission regulation in the reduction of PAHs emissions and provided a combined range for identifying PAHs traffic emission sources.