Optimized two-level placement of test points for multi-objective air monitoring of the Three Gorges Reservoir area

To fit the complicated geographic conditions of the Three Gorges Reservoir area, a two-level multi-objective monitoring system was developed to monitor the atmosphere of the area. Statistical analysis of environmental monitoring data and the macro control principle were employed to configure the upper layer. The lower layer was designed by the application of the thumb rule to a local terrain and specific point sources of pollution therein. The optimized two-level system comprises an upper layer of 16 monitoring stations distributed at places of diverse geographical, ecological, economical and social characteristics, and a lower layer of ]6 sub-machines at each monitoring station of the upper layer. This optimal outcome fits the complicated conditions of the Three Gorges Reservoir area, substantially cuts down the installation cost and the operation cost, and provides accurate monitoring data of atmosphere over the entire area with a high resolution.

Air quality nearby road traffic tunnel portals: BTEX monitoring

A monitoring campaign of BTEX （benzene, toluene, ethylbenzene, o- m- and p-xylene） was carried out nearby two tunnel portals in the urban area of Naples with the aim to verify air quality in this kind of urban sites. Sampling was carried out using the active adsorption technique. Sampling time was 1 h. Ambient temperature and traffic flow measurements were carried out during each sampling operation. The results indicate that average benzene concentrations at both sites exceed the limit value of 10 μg/Nm^3 established by the European Community （EC） （Dir. 2000/69）. Concentration levels of other BTEX are relatively high as well. A correlation between BTEX concentration and two wheeler vehicle flow was observed.

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.

Optimization of Air Quality Monitoring Network Using GIS Based Interpolation Techniques

This paper proposes a simple method of optimizing Air Quality Monitoring Network (AQMN) using Geographical Information System (GIS), interpolation techniques and historical data. Existing air quality stations are systematically eliminated and the missing data are filled in using the most appropriate interpolation technique. The interpolated data are then compared with the observed data. Pre-defined performance measures root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r) were used to check the accuracy of the interpolated data. An algorithm was developed in GIS environment and the process was simulated for several sets of measurements conducted in different locations in Riyadh, Saudi Arabia. This methodology proves to be useful to the decision makers to find optimal numbers of stations that are needed without compromising the coverage of the concentrations across the study area.

Air Pollution Concentration Approach to Potential Area Selection of the Air Quality Monitoring Station in Nakhon Ratchasima Municipality, Thailand

The purpose of the study is to generate traffic air information system） to determine a proper zone of AQMS （air analyzed were carbon monoxide （CO）, and nitrogen oxides （NOx） pollution map using mathematical model and GIS （geographic quality monitoring station） in municipality area. The pollutants which can be harmful to people living in the area. The three steps of mapping process were performed under the GIS environment using the existing vehicle emission rates and pollutant dispersion model. First, traffic volume, road network, and the emission rates of road segments varying with types of vehicle were collected from existing data. Second, the pollutant concentrations were calculated by use of CALINE4, a tool with Gaussian dispersion model. The model parameters include emission rate, wind directions and speeds, ambient temperature and observed pollutant concentration, and atmospheric stability during all seasons from the January 1, 2010 to May 31,2011 with regardless the rainy season. This resulted in concentrations at many receptor points along links of the road network. Third, distributions of pollution concentrations were generated by means of the spatial interpolation of those from receptors. The results of pollution raster-based maps are used for determining frequency of violence and combined pollution map. The resulting frequency of violence and intensity concentration will be further integrated to determine a potential area of AQMS. Finally, achieving pollution potential area of AQMS can be located as helpful basic data for efficient traffic and transportation planning.

Development of the Program of Monitoring of Composition of Atmospheric Air

To settle the indicators of air pollutions at the industrial enterprises is very important. For this purpose, authors analyzed and developed a program for monitoring atmospheric composition in Borland Delphi 7 for the calculation of the hazard class and the average daily maximum allowable concentration of air. Results of calculations on this program will allow operating composition of air by regulation of operating modes of filtering devices.

Air Quality Monitoring and Its Implication on the Environmental Licensing Process in Brazil

In the state of Sao Paulo, Brazil, public policies regarding the air quality aimed at the welfare of the population are strongly dependent on monitoring conducted by the Sao Paulo State Environmental Company (CETESB), which can be influenced by faulty monitors and equipment support and cuts in power supply, among others. A research conducted from 1998 to 2008 indicated that a significant portion of the air quality automatic stations in the state of Sao Paulo did not meet the criterion of representativeness of measurements of PM10, NO2, O3, CO and SO2 concentrations which resulted in the classification of some municipalities as the nonattainment area, a situation evidenced for PM10 and O3 parameters. The network unavailability for each parameter was estimated and compared with the monitoring networks operated in Canada and the UK. This paper discusses the implications of the lack of representativeness of measurements in the environmental licensing process of pollution sources from 2008, when by the effect of state law, municipalities have been qualified according to their air quality nonattainment level.

Investigating the Air Quality Parameters in Louisiana’s Industrial Corridor: A Baton Rouge Case Study

In a local context, sustainable development entails utilizing the current resources—material and immaterial, measurable and immeasurable, popular and unpopular—of the community in a manner that avoids overexploitation and ensures intergenerational equity. This approach prioritizes the safety and health of local citizens, placing communal productivity above corporate profitability. This research aims to assess air quality surrounding 28 chemical industry sites in Baton Rouge, Louisiana, to understand the environmental and health impacts of industrial pollutants, with a focus on environmental justice. Air quality pollutants, including PM2.5, PM10, O3, NO2, CO, and SO2, were monitored for 75 days during the Summer, using the BreezoMeter app. Python, Mapize, and QGIS software technologies were utilized for data analysis and visualization. Findings indicate a reduction in NO2 and CO levels, compared to existing literature. However, the persistent challenge of particulate matter suggests areas for further environmental management efforts. Additionally, the research suggests a significant disparity in air pollution exposure, probably affecting marginalized communities. Although the nature of the study might not fully capture annual pollution trends, the findings highlight the urgent need for the chemical industry to adopt efficient production methods and for policymakers to enhance air quality standards and enforcement, particularly in pollution-sensitive areas. The disproportionate impact of air pollution on vulnerable communities calls for a more inclusive approach to environmental justice, ensuring equitable distribution of clean air benefits and community involvement in pollution management decisions.

Measurements of Fission Products from the Fukushima Daiichi Incident in San Francisco Bay Area Air Filters,Automobile Filters,Rainwater,and Food

A variety of environmental media were analyzed for fallout radionuclides resulting from the Fukushima nuclear accident by the Low Background Facility (LBF) at the Lawrence Berkeley National Laboratory (LBNL) in Berkeley, CA. Monitoring activities in air and rainwater began soon after the onset of the March 11, 2011 tsunami and are reported here through the end of 2012. Observed fallout isotopes include 131I, 132I, 132Te, 134Cs, 136Cs, and 137Cs. Isotopes were measured on environmental air filters, automobile filters, and in rainwater. An additional analysis of rainwater in search of 90Sr is also presented. Last, a series of food measurements conducted in September of 2013 are included due to extended media concerns of 134,137Cs in fish. Similar measurements of fallout from the Chernobyl disaster at LBNL, previously unpublished publicly, are also presented here as a comparison with the Fukushima incident. All measurements presented also include natural radionuclides found in the environment to provide a basis for comparison.

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.

Ultrasensitive Indium Phosphide Nanomembrane Wearable Gas Sensors

Air quality is deteriorating due to continuing urbanization and industrialization.In particular,nitrogen dioxide(NO_(2))is a biologically and environmentally hazardous byproduct from fuel combustion that is ubiquitous in urban life.To address this issue,we report a high-performance flexible indium phosphide nanomembrane NO_(2)sensor for real-time air quality monitoring.An ultralow limit of detection of~200 ppt and a fast response have been achieved with this device by optimizing the film thickness and doping concentration during indium phosphide epitaxy.By varying the film thickness,a dynamic range of values for NO_(2)detection from parts per trillion(ppt)to parts per million(ppm)level have also been demonstrated under low bias voltage and at room temperature without additional light activation.Flexibility measurements show an adequately stable response after repeated bending.On-site testing of the sensor in a residential kitchen shows that NO_(2)concentration from the gas stove emission could exceed the NO_(2)Time Weighted Average limit,i.e.,200 ppb,highlighting the significance of real-time monitoring.Critically,the indium phosphide nanomembrane sensor element cost is estimated at<0.1 US$due to the miniatured size,nanoscale thickness,and ease of fabrication.With these superior performance characteristics,low cost,and real-world applicability,our indium phosphide nanomembrane sensors offer a promising solution for a variety of air quality monitoring applications.

Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved interval partial least squares

As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring system.In order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs spectra.This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling.The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum.Different wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory.When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum wavelengths.While the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and 0.9999.The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.

A methodological framework for improving air quality monitoring network layout.Applications to environment management

This work aims to provide a methodology framework which allows to improve the performance and efficiency of an air quality monitoring network(AQMN).It requires to be constituted by a minimum and reliable number of measurement sites.Nevertheless,the AQMN efficiency should be assessed over time,as a consequence of the possible emergence of new emission sources of air pollutants,which could lead to variations on their spatial distribution within the target area.PM_(10)particles data monitored by the Community of Madrid's(Spain)AQMN between 2008 and 2017 were used to develop a methodology to optimize the AQMN performance.The annual spatial distribution of average PM_(10)levels over the studied period monitored by all current stations vs those more representative was provided by a geographic information system(GIS),and the percentage of similarity between both postulates was quantified using simple linear regression(>95%).As one innovative tool of this study,the practical application of the proposed methodology was validated using PM_(10)particles data measured by AQMN during 2007 and 2018,reaching a similitude degree higher than 95%.The influence of temporal variation on the proposed methodological framework was around 20%.The proposed methodology sets criteria for identifying non-redundant stations within AQMN,it is also able to appropriately assess the representativeness of fixed monitoring sites within an AQMN and it complements the guidelines set by European legislation on air pollutants monitoring at fixed stations,which could help to tackle efforts to improve the air quality management.

Multi-sensing paradigm based urban air quality monitoring and hazardous gas source analyzing:a review

Effectively monitoring urban air quality,and analyzing the source terms of the main atmospheric pollutants is important for public authorities to take air quality management actions.Previous works,such as long-term obser-vations by monitoring stations,cannot provide customized data services and in-time emergency response under urgent situations(gas leakage incidents).Therefore,we first review the up-to-date approaches(often machine learning and optimization methods)with respect to urban air quality monitoring and hazardous gas source anal-ysis.To bridge the gap between present solutions and practical requirements,we design a conceptual framework,namely MAsmed(Multi-Agents for sensing,monitoring,estimating and determining),to provide fine-grained concentration maps,customized data services,and on-demand emergency management.In this framework,we leverage the hybrid design of wireless sensor networks(WSNs)and mobile crowdsensing(MCS)to sense urban air quality and relevant data(e.g.traffic data,meteorological data,etc.);Using the sensed data,we can create a fine-grained air quality map for the authorities and relevant stakeholders,and provide on-demand source term estimation and source searching methods to estimate,seek,and determine the sources,thereby aiding decision-makers in emergency response(e.g.for evacuation).In this paper,we also identify several potential opportunities for future research.

Spatial Analysis of Fine Particulate Matter (PM2.5) in South St. Boniface and Mission Industrial Area, Winnipeg, Manitoba, Canada

Particulate matter smaller than 2.5 microns (PM2.5) stays airborne for long periods and can enter the lungs, increasing respiratory and cardiovascular risks. Metal shredders are known sources of PM2.5, lead and other heavy metals. Winnipeg residents of South Saint Boniface (SSB) in Manitoba, Canada, live downwind of the Mission Industrial Area (MIA), which includes a metal shredder, train tracks and other industries. Residents are concerned about the MIA air and noise pollution and wanted ambient air quality monitoring in their mixed land-use area to understand its impact on their health. We measured and mapped the daytime PM2.5, from the MIA and South St. Boniface (SSB) neighborhoods using the Dylos DC 1700 PM over seven months. The Dylos air quality data for PM2.5 was validated by the two federal reference monitors in the city, finding a moderate to very strong correlation (r = 0.52 to 0.83;p-value 0.001), confirming good accuracy. A spatial analysis of the emission data showed that the highest pollution concentration was downwind of the scrap metal shredder in MIA. One-way ANOVA and Pearson correlation analysis revealed significantly higher levels of PM2.5 at MIA and SSB than at the reference sites, which are away from pollution sources. The PM2.5 Canadian Ambient Air Quality Standard (CAAQS) of 27 μg/m3 was exceeded downwind of the property line of the scrap metal shredder in the MIA for five of the 35 monitoring days averaging between 28.9 μg/m3 to 38.1 μg/m3 over eight hours. The standard was not exceeded in the residential area, although PM2.5 levels higher than background levels increased SSB residents exposure levels. This exceedance of regulatory standards requires action to reduce emissions.

Assessment of PM2.5 Distribution at Different Heights in Hanoi, Vietnam

Monitoring of PM10 and PM2.5 concentrations frequently is essential for assessing air quality and informing pollution control strategies. This study examines the effect of height on PM2.5 distribution in Hanoi using EPA-standard methods at five rooftop locations on high-rise buildings. Results from Phase 1 (pre-pollution period) indicate a nearly 50% reduction in PM2.5 concentration, decreasing from 34.76 μg/m3 at 40 m to 13.95 μg/m3 at 336 m. In contrast, Phase 2 (pollution wave) showed relatively stable PM2.5 concentrations across heights, likely influenced by cold air masses and wind speed. MLR and MNLR analyses reveal the significant impact of meteorological factors and PM10 on PM2.5 levels, with the MNLR model accounting for 80% - 94% of the variance, outperforming the MLR model’s 50% - 80%. Employing UAVs, Lidar, and synchronized meteorological data is proposed as an advanced approach to enhance the accuracy of height-based dust concentration assessments.

Data augmentation for bias correction in mapping PM_(2.5) based on satellite retrievals and ground observations

As most air quality monitoring sites are in urban areas worldwide,machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants.The bias stems from the issue of dataset shift,as the density distributions of predictor variables differ greatly between urban and rural areas.We propose a data-augmentation approach based on the multiple imputation by chained equations(MICE-DA)to remedy the dataset shift problem.Compared with the benchmark models,MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM2.5 in the megacity(Chengdu)at the foot of the Tibetan Plateau,especially for correcting the estimation bias,with the mean bias decreasing from-3.4µg/m3 to-1.6µg/m3.As a complement to the holdout validation,the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM2.5 over the study area.The essence of MICE-DA is strengthening the correlation between PM2.5 and aerosol optical depth(AOD)during the data augmentation.Consequently,the importance of AOD is largely enhanced for predicting PM2.5,and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5%to 18.4%.This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies.The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.

A fully electronic microfabricated gas chromatograph with complementary capacitive detectors for indoor pollutants

This paper reports a complete micro gas chromatography(μGC)system in which all the components are lithographically microfabricated and electronically interfaced.The components include a bi-directional Knudsen pump,a preconcentrator,separation columns and a pair of capacitive gas detectors;together,these form the iGC3.c2 system.All the fluidic components of the system are fabricated by a common three-mask lithographic process.The Knudsen pump is a thermomolecular pump that provides air flow to theμGC without any moving parts.The film heaters embedded in the separation columns permit temperature programming.The capacitive detectors provide complementary response patterns,enhancing vapor recognition and resolving coeluting peaks.With the components assembled on printed circuit boards,the system has a footprint of 8×10 cm^(2).Using room air as the carrier gas,the system is used to experimentally demonstrate the analysis of 19 chemicals with concentration levels on the order of parts per million(p.p.m.)and parts per billion(p.p.b.).The tested chemicals include alkanes,aromatic hydrocarbons,aldehydes,halogenated hydrocarbons and terpenes.This set of chemicals represents a variety of common indoor air pollutants,among which benzene,toluene and xylenes(BTX)are of particular interest.

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.