Design Of Air Quality Monitoring Networks Research Articles

Optimising the network of air quality monitoring stations

This study explores the application of genetic algorithms (GAs) for optimizing the network of air quality monitoring stations. Recognizing the complexity of accurately assessing air pollution levels across diverse urban and rural landscapes, the research focuses on finding the most effective station placements to maximize coverage and data fidelity while minimizing costs. The methodology entails simulating natural selection processes, including selection, crossover, and mutation, to evolve a population of potential solutions. Each candidate solution in the population represents a unique configuration of monitoring stations. A fitness function evaluates the efficiency of each configuration based on criteria such as population coverage, proximity to pollution sources, and installation and operational expenses. The research employs a genetic algorithm developed in Python, which iteratively refines the population of solutions over thousands of generations. The algorithm's performance is assessed through experimental validation, with an emphasis on the adaptability of the approach to accommodate various environmental, economic, and regulatory constraints. Results indicate that GAs can effectively balance multiple optimization objectives, leading to a network design that is both cost-efficient and comprehensive in its monitoring capabilities. The outcome of the study is an optimized network that significantly improves upon the initial state in terms of coverage and cost-effectiveness. The study concludes that genetic algorithms offer a promising avenue for addressing the challenges of air quality monitoring network design. The flexibility and global search capabilities of GAs make them suitable for the complex, multi-objective nature of the task. Moreover, the findings suggest potential for further improvements and applications of GAs in environmental monitoring and other complex systems optimization scenarios.

An optimized development of urban air quality monitoring network design based on particulate matters.

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500m * 500m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

Robust optimization for designing air quality monitoring network in coal ports under uncertainty

Air quality monitoring network (AQMN) is an important management tool to mitigate the impact of air pollution from coal port operations on coastal atmospheric environment and public health. The location of monitoring stations in the AQMN is influenced by wind conditions and emission intensity of air pollutants, which is highly related to the efficiency of coal port operations. Therefore, this paper aims at solving the problem of how to design the AQMN with consideration of uncertain operational efficiency and wind conditions. This study proposes a robust optimization method to address the impact of uncertainty on the design of AQMN in coal ports. Firstly, the deterministic AQMN design problem is summarized as a maximum weighted covering location problem (MWCLP) and formulated as a gradual covering model with the cooperative covering strategy. Then, a linear approximation method is applied to the non-linear model to improve computational efficiency. Secondly, a method for constructing uncertain scenarios is proposed by combining sampling method, port emission inventory, and atmospheric diffusion simulation. After that, a robust optimization model is developed based on the concept of p-robustness to ensure that the relative regret value of each uncertain scenario is less than a specified threshold. Finally, we take a coal port in northern China as a case study. Results show that the average monitoring capacity of robust layout is 5.25% and 1.57% higher than that of conventional layout and deterministic layout. The robust layout ensures a relative regret value of no more than 3.41% for each scenario. The proposed method can provide port and environmental authorities with robust decision support of AQMN design under limited budget and uncertainty of operational efficiency and wind conditions.

Air Quality Monitoring Network Design to Control Nitrogen Dioxide and Ozone, Applied in Granada, Spain

This paper describes a method to design air quality monitoring networks for nitrogen dioxide and ozone and its application in Granada, a city located in Andalusia, southern Spain. The city has a population of 236,988 inhabitants, and traffic is its main source of air pollution. Sampling campaigns with passive diffusion samplers at 88 sites were carried out to obtain information on the pollution distribution in Granada. The average concentrations found for NO2 and O3 were 36.5 μg/m3 and 51.6 μg/m3, respectively. Maximum values of up to 57.1 μg/m3 NO2 were found in Granada city center and O3 reached 77.2 μg/m3 downwind from the emission source. After spatial interpolation of the obtained values with Geographical Information Systems, a selection of the best locations for the monitoring stations was made, in line with the macro- and microscale siting requirements of the European Directive 2008/50/EC on ambient air quality and cleaner air for Europe. Another sampling campaign with diffusive samplers was carried out in 2007 to determine if the locations of the air quality assessment stations were still representative for their zone. A correction was made in the control network following results of this verification campaign.

Air quality monitoring network design to control nitrogen dioxide and ozone, applied in Malaga, Spain

Air monitoring networks are necessary to assess air quality in order to reduce pollution to levels which minimize harmful effects on human health and the environment. This paper describes a method to design or optimize air quality monitoring networks for nitrogen dioxide and ozone and its application in Malaga, a medium large city located in Andalusia, southern Spain, with traffic being the main source of air pollution. The completion of this method revealed that the old assessment network in Malaga was badly designed and made it possible to determine that one traffic-orientated and one background control station were necessary for NO 2 assessment in Malaga, as well as two control stations for O 3. First the number of stations necessary is obtained from historical data. Sampling campaigns with passive diffusion samplers at 74 sites were then carried out to obtain information on the pollution distribution in Malaga. The average concentrations found for NO 2 and O 3 were 22.8 μg/m 3 and 64.3 μg/m 3 respectively. Maximum values of up to 42.2 μg/m 3 NO 2 were found in Malaga city centre and O 3 reached 91.5 μg/m 3 downwind from the emission source. After spatial interpolation of the obtained values with Geographical Information Systems, a selection of the best locations for the monitoring stations was made, in line with the macro- and microscale siting requirements of the European Directive 2008/50/EC on ambient air quality and cleaner air for Europe.

A heuristic optimization approach for Air Quality Monitoring Network design with the simultaneous consideration of multiple pollutants

An interactive optimization methodology for allocating the number and configuration of an Air Quality Monitoring Network (AQMN) in a vast area to identify the impact of multiple pollutants is described. A mathematical model based on the multiple cell approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. These spatial temporal patterns were subject to a heuristic optimization algorithm to identify the optimal configuration of a monitoring network. The objective of the optimization is to provide maximum information about multi-pollutants (i.e., CO, NO x and SO 2) emitted from each source within a given area. The model was applied to a network of existing refinery stacks and the results indicate that three stations can provide a total coverage of more than 70%. In addition, the effect of the spatial correlation coefficient ( R C ) on total area coverage was analyzed. The modeling results show that as the cutoff correlation coefficient R C is increased from 0.75 to 0.95, the number of monitoring stations required for total coverage is increased. A high R C based network may not necessarily cover the entire region, but the covered region will be well represented. A low R C based network, on the other hand, would offer more coverage of the region, but the covered region may not be satisfactorily represented.

Ambient air quality monitoring network design for assessing human health impacts from exposures to airborne contaminants.

Existing methods of establishing ambient air quality monitoring networks typically evaluate only parameters related to ambient concentrations of the contaminant(s) of interest such as emission source characteristics, atmospheric transport and dispersion, secondary reactions, deposition characteristics, and local topography. However, adverse health risks from exposures to airborne contaminants are a function of the contaminant and the anatomic and physiologic characteristics of the exposed population. Thus, ambient air quality monitoring networks designed for the protection of public health or for epidemiological studies evaluating adverse health impacts from exposures to ambient air contaminants should account for both contaminant characteristics and human health parameters. A methodology has been established which optimizes ambient air quality monitoring networks for assessments of adverse human health impacts from exposures to airborne contaminants by incorporating human health risk assessment techniques. The use of risk assessment techniques as the basis for designing ambient air quality monitoring networks will help to target limited financial and human resources to evaluate human health risks from exposures to airborne contaminants.