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 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.

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.

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.

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.

Satellite remote sensing of atmospheric particulate matter mass concentration:Advances,challenges,and perspectives

Mapping the mass concentration of near-surface atmospheric particulate matter(PM)using satellite observations has become a popular research niche,leading to the development of a variety of instruments,algorithms,and datasets over the past two decades.In this study,we conducted a holistic review of the major advances and challenges in quantifying PM,with a specific focus on instruments,algorithms,datasets,and modeling methods that have been developed over the past 20 years.The aim of this study is to provide a general guide for future satellite-based PM concentration mapping practices and to better support air quality monitoring and management of environmental health.Specifically,we review the evolution of satellite platforms,sensors,inversion algorithms,and datasets that can be used for monitoring aerosol properties.We then compare various practical methods and techniques that have been used to estimate PM mass concentrations and group them into four primary categories:(1)univariate regression,(2)chemical transport models(CTM),(3)multivariate regression,and(4)empirical physical approaches.Considering the main challenges encountered in PM mapping practices,for example,data gaps and discontinuity,a hybrid method is proposed with the aim of generating PM concentration maps that are both spatially continuous and have high precision.

Evaluation of health benefit using Ben MAP-CE with an integrated scheme of model and monitor data during Guangzhou Asian Games

Guangzhou is the capital and largest city（land area：7287 km2）of Guangdong province in South China.The air quality in Guangzhou typically worsens in November due to unfavorable meteorological conditions for pollutant dispersion.During the Guangzhou Asian Games in November 2010,the Guangzhou government carried out a number of emission control measures that significantly improved the air quality.In this paper,we estimated the acute health outcome changes related to the air quality improvement during the 2010 Guangzhou Asian Games using a next-generation,fully-integrated assessment system for air quality and health benefits.This advanced system generates air quality data by fusing model and monitoring data instead of using monitoring data alone,which provides more reliable results.The air quality estimates retain the spatial distribution of model results while calibrating the value with observations.The results show that the mean PM2.5concentration in November 2010 decreased by 3.5μg/m^3 compared to that in 2009 due to the emission control measures.From the analysis,we estimate that the air quality improvement avoided 106 premature deaths,1869 cases of hospital admission,and 20,026 cases of outpatient visits.The overall cost benefit of the improved air quality is estimated to be 165 million CNY,with the avoided premature death contributing 90%of this figure.The research demonstrates that Ben MAP-CE is capable of assessing the health and cost benefits of air pollution control for sound policy making.