Ever since air pollution became a national concern in the 1950's, more and more emphasis has been placed on collection of representative air samples for many purposed, to include (1) evaluation of the degree to which national ambient air quality standards are being met and (2) to monitor maximum emission levels from point sources. Until recently efforts were directed toward qualitative methods of siting monitors for representative sampling. Since the dispersion of effluents is most complex, the quality of the data collected on the basis of judgment and, more or less, incremental siting about the source, has become suspect. Furthermore, with the increasing demands for monitoring due to international growth in network monitoring systems, amendments to the Clean Air Act and the legislation on the Prevention of Significant Deteoriation of Air Quality, it is not cost-effective to encircle point sources with large numbers of equally spaced monitors. This paper discussed the history of air pollution concerns that have resulted in the need for monitoring; the development of siting techniques through largely qualitative measures; and finally, summarizes three quantitative methodologies for monitoring point sources. Emphasis is placed on the methodology developed by Noll, et al., (1977), based on the author's belief that this methodology represents the state of the art.

Thesis (M. Sc.)--University of Hong Kong, 1999. / Includes bibliographical references.

published_or_final_version / Urban Planning and Environmental Management / Doctoral / Doctor of Philosophy

Air pollution sampling site selection is one of the most important and yet most vexing of the problems faced by those responsible for regional and urban air quality management and for the attainment and maintenance of national ambient air quality standards. Since one cannot hope to monitor air quality at all locations at all times, selection of sites to give a reliable and realistic picture of air quality becomes a major issue and at the same time a difficult task. The location (configuration) and the number of stations may be based on many factors, some of which may depend on limited resources, federal and state regulations and local conditions. The combination of these factors has made air quality surveys more complex; requiring comprehensive planning to ensure that the prescribed objectives can be attained in the shortest possible time and at the least cost. Furthermore, the choice and siting of the measuring network represents a factor of significant economic relevance for policymakers. In view of the fact that equipment, maintenance and operating personnel costs are increasing dramatically, the possibility of optimizing the monitoring design, is most attractive to the directors of air quality management programs. In this work a methodology that is able to design an optimal air quality monitoring network (AQMN) is described. The objective of the optimization is to provide maximum information about the presence and level of atmospheric contaminants in a given area and with a limited budget. A criterion for assessing the allocation of monitoring stations is developed by applying a utility function that can describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. 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. This data was used to train artificial neural networks (ANN) that were proven to be able to predict very well the pattern and violation scores at different potential locations. These neural networks were embedded within a mathematical programming model whose objective is to determine the best monitoring locations for a given budget. This resulted in a nonlinear program (NLP). The proposed model is applied to a network of existing refinery stacks and the locations of monitoring stations and their area coverage percentage are obtained.

Air pollution sampling site selection is one of the most important and yet most vexing of the problems faced by those responsible for regional and urban air quality management and for the attainment and maintenance of national ambient air quality standards. Since one cannot hope to monitor air quality at all locations at all times, selection of sites to give a reliable and realistic picture of air quality becomes a major issue and at the same time a difficult task. The location (configuration) and the number of stations may be based on many factors, some of which may depend on limited resources, federal and state regulations and local conditions. The combination of these factors has made air quality surveys more complex; requiring comprehensive planning to ensure that the prescribed objectives can be attained in the shortest possible time and at the least cost. Furthermore, the choice and siting of the measuring network represents a factor of significant economic relevance for policymakers. In view of the fact that equipment, maintenance and operating personnel costs are increasing dramatically, the possibility of optimizing the monitoring design, is most attractive to the directors of air quality management programs. In this work a methodology that is able to design an optimal air quality monitoring network (AQMN) is described. The objective of the optimization is to provide maximum information about the presence and level of atmospheric contaminants in a given area and with a limited budget. A criterion for assessing the allocation of monitoring stations is developed by applying a utility function that can describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. 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. This data was used to train artificial neural networks (ANN) that were proven to be able to predict very well the pattern and violation scores at different potential locations. These neural networks were embedded within a mathematical programming model whose objective is to determine the best monitoring locations for a given budget. This resulted in a nonlinear program (NLP). The proposed model is applied to a network of existing refinery stacks and the locations of monitoring stations and their area coverage percentage are obtained.

Air quality was influenced by many factors, in South Taiwan, air pollutants transportation caused by monsoon or sea-land breeze that may caused high air pollution events. Air pollutant generated by human activity on daytime, then transported and accumulated at sea region by land breeze during the nighttime. Unfortunately, air pollutants that accumulated over sea on night may transport back to land by sea breeze on daytime. Besides, monsoon may carry air pollutants from other regions to South Taiwan and caused high air quality event. Till now, air quality influenced by sea-land breeze and monsoon were not verified in South Taiwan. This study investigated the temporal variation and spatial distribution of air pollutants in the atmosphere around the coastal region of South Taiwan. Ambient air pollutants were simultaneously monitored both inland and offshore. Inland monitoring was conducted at two sites associated with fourteen national air quality monitoring stations, while offshore monitoring was conducted at two sites both in an island and on the boat. A protocol of ambient air quality monitoring was conducted for forty-eight hours. Gaseous air pollutants (i.e. CO, SO2, NOX, THC, and O3) were continuously monitored instrumentally. Data obtained from both inland and offshore monitoring were applied to plot the concentration contour by a software SURFER. Hourly variation of air pollutant concentrations was further used to study the influences of sea-land breezes on the transportation of air pollutants around the coastal region of South Taiwan for different seasons. In August and November, 2006 and May, 2007, sea-land breeze was observed during sampling period and sea breeze arise from 9:00 A.M. to 24:00 P.M. The average wind velocity was 1~4 m/s during the sampling period. In January and March, 2007, prevail wind direction was north direction and northeast direction (270o~30o), that was influenced by northeast monsoon during the sampling period. The average wind velocity was 2~4 m/s. The results showed that distribution of air pollutants, including O3, NOX, THC, and CO influenced by sea-land breezes, particularly for ozone. Air pollutants transported to sea region during the nighttime, and transported back at daytime. This phenomenon cause air pollutants accumulated between Kao-Ping and sea region. In general, NOX generated by transportation and industrial process, thus high concentration of NOX appeared during traffic congestion period and at industry region, mainly Kaohsiung city and Linyuang industrial region. However, sea-land breeze effect upon transportation of air pollutants wasn¡¦t obvious for SO2. High SO2 concentration appeared at Linyuang industrial region and Siaogang at daytime, and transported to region along the coast. During northeastern monsoon season, northeast winds obstructed by Central Mountain Range cause air pollutants accumulated at Kao-Ping region. High NOX concentration appeared at Kaohsiung City and Linyuang industrial region. SO2 accumulated at Siaogang and Linyuang during the nighttime might be caused by high atmospheric pressure system and blew air pollutants to Linbian. CO was accumulated at Siaogang at daytime and transported to Donggang, while THC was accumulated at Donggang whole day.

No description available.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 261-267).

Hazards associated with air pollution motivate the search for technologies capable of monitoring individual exposure to gaseous pollutants and particulate matter (PM). A Low-cost Optical Particle Counter (OPC), costing less than 50 USD, is an example of such technologies. Currently, OPCs are widely used to measure the concentration of particle matter in ambient air. While these low-cost air quality sensors are widely available, the accuracy and precision of these devices is highly uncertain. Consequently, the purpose of this thesis is to present an analytical model of two generic, low-cost OPCs based on the Laws of Conservation of Mass, Momentum, and Energy. These models utilize Mie scattering theory and Computational Fluid Dynamics models to quantify uncertainty and accuracy in low-cost OPCs based first principles. Modeling results indicate that the measurement of forward-scattered light may dramatically increase the accuracy of low-cost OPCs. These results also indicate that careful attention must be placed on the design of sensor flow passages so as to most efficiently transport particles to the scattering volume where they may be detected. A combination of careful attention to photodetector placement in the forward scattering regime as well as efficient transport to the scattering volume may increase low-cost OPC accuracy by magnitudes of order.

Background: Air pollution has become a serious environmental issue. It is responsible for hundreds of fatalities each year and it poses a serious threat to human health and environment. It leads to global warming, greenhouse effect and it also causes respiratory problems like asthma, lung cancer etc. It is important to predict the quality of the air to regulate air pollution. Air quality index (AQI) is a measure of air quality which describes the level of air pollution. Machine learning algorithms can help in predicting the AQI. Linear regression, LASSO regression, ridge regression, and SVR algorithms were used to forecast the AQI.  Objectives: The main objective of the thesis is to build and train a models using machine learning algorithms and find out the most accurate model in predicting the AQI.  Methods: Literature Review and Experimentation were chosen as methods to answer the research questions. There are a number of research papers written on prediction of AQI and literature review helped us a lot in research and references. Experimentation is also used to find out the most accurate machine learning model in predicting the air quality. In the experimentation phase, four machine learning algorithms were trained with air quality data to create predictive models for fore- casting AQI.  Results: Algorithms like Logistic Regression, Ridge Regression, LASSO Regression, and SVR are selected through literature review. Upon experimentation and training the algorithm with "Air Quality Data in India (2015-2020)" data set has showed that Ridge regression has the least MAE and RMSE and the highest R- square, which shows that it has the highest performance in predicting the AQI.  Conclusions: Four models are built by training with machine learning algorithms like Logistic Regression, Ridge Regression, LASSO Regression, and SVR and "Air Quality Data in India (2015-2020)" data set. After experimentation, it was found that Ridge Regression and LASSO regression has the better performance in the prediction of AQI.