Multi-objective optimization of air quality monitoring

A new method for multi-objective optimization of air quality monitoring systems based on satellite remote sensing of the troposphere is described in this work. The technique uses atmospheric turbidity as surrogate for air pollution loading. Through inverse chemical modeling and ancillary information the respective patterns of primary gaseous and particle pollutants are inferred. The optimization algorithm uses the resulting maps of ambient air pollution as input. It focuses on the gain of information with regard to human exposure to high pollution, potential impact on cultural heritage, compliance to ambient air quality standards, monitoring key point and area source emissions, as well as on the associated cost. Application of the method in Brescia, Italy showed its significant potential for improving the cost-effectiveness of air quality monitoring networks at the urban and regional scales.     

Methodology for designing air quality monitoring networks: II. Application to Las Vegas,Nevada, for carbon monoxide

An objective methodology presented in a companion paper (Liu et al., 1986) for determining the optimum number and disposition of ambient air quality stations in a monitoring network for carbon monoxide is applied to the Las Vegas, Nevada, area. The methodology utilizes an air quality simulation model to produce temporally-varying air quality patterns for each of a limited number of meteorological scenarios representative of the region of interest. These air quality patterns in turn serve as the data base in a two-step procedure for the identification and ranking of the most desirable monitoring locations (step 1) and the removal of redundancies in spatial coverage among the desired locations (step 2.)The performance of the air quality simulation model, a key element in the design methodology, was evaluated in the Las Vegas area in a special field measurement program. In the Las Vegas demonstration for carbon monoxide, 19 stations covering concentration maxima and 3 stations covering background concentrations in rural areas were identified and ranked. A 10-station network, for example, consisting of 7 stations for peak average concentrations and 3 stations for background concentrations, was selected for a desired minimum detection capability of 50% of concentration variations. Networks with fewer stations would be selected if smaller minimum detection capabilities of concentration variations are acceptable, and vice versa. Background stations could, of course, be deleted for networks with the sole purpose of discerning peak concentrations.Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency through Contract No. 68-03-2446 to Systems Application, Inc., it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

The design of cost-effective air quality monitoring networks

A statistical measure of information content is used as a means for assessing the effectiveness of a particular monitoring network configuration. This measure is directly related to the precision with which the network can estimate the level of air quality in the region. Each potential site in the network can contribute to an increase in the overall precision of the estimate. The amount of increase is determined by the degree of dependence, displayed by observed pollutant concentrations, between potential sites.A statistical procedure is used to combine the observed pollution dynamics and the measurement network in order to assess network effectiveness. An iterative application of this procedure leads to the design of the most effective network subject to a specified budget constraint.     

Application of statistical modeling to optimize a coastal water quality monitoring program

The long-term water quality monitoring program implemented by the Massachusetts Water Resources Authority in 1992 is extensive and has provide substantial understanding of the seasonality of the waters in both Boston Harbor and Massachusetts Bay and the response to improvements in effluent quality and offshore transfer of the effluent in September 2000. The monitoring program was designed with limited knowledge of spatial and temporal variability and long-term trends within the system. This led to an extensive spatial and temporal sampling program. The data through 2003 showed high correlation within physical parameters measured (e.g., salinity, dissolved oxygen) and in biological measures such as chlorophyll fluorescence. To address the potential sampling redundancies in the measurement program, an assessment of the impact of reduced levels of monitoring on the ability to make water quality decisions was completed. The optimization was conducted by applying statistical models that took into account whether there was evidence of a seasonal pattern in the data. The optimization used model survey average readings to identify temporal fixed effects, model survey-average-corrected individual station readings to identify spatial fixed effects, corrected the individual station readings for temporal and spatial fixed effects and derived a correlation model for the corrected data, and applied the correlation model to characterize the correlation of annual average readings from reduced monitoring programs with true parameter levels. Reductions in the number of sampling stations were found less detrimental to the quality of the data for annual decision-making than reductions in the number of surveys per year, although there is less of a difference in this regard for dissolved oxygen than there is for chlorophyll. The analysis led to recommendations for a substantially lower monitoring effort with minimal loss of information. The recommendation supported an annual budget savings of approximately $183,000. Most of the savings was from fewer surveys as approximately $21,000 came from the reduction in the number of stations monitored from 21 to 7 and associated laboratory analytical costs.     

Methodology for designing air quality monitoring networks: I. Theoretical aspects

An objective methodology is presented for determining the number and disposition of ambient air quality stations in a monitoring network for the primary purpose of compliance with air quality standards. The methodolgy utilizes a data base with real or simulated data from an air quality dispersion model for application with a two-step process for ascertaining the optimal monitoring network. In the first step, the air quality patterns in the data base are collapsed into a single composite pattern through a figure-of-merit (FOM) concept. The most desirable locations are ranked and identified using the resultant FOM fields. In the second step the network configuration is determined on the basis of the concept of spheres of influence (SOI) developed from cutoff values of spatial correlation coefficients between potential monitoring sites and adjacent locations. The minimum number of required stations is then determined by deletion of lower-ranked stations whose SOIs overlap. The criteria can be set to provide coverage of less than some fixed, user-provided percentage of the coverage of tha SOIs of the higher ranked stations and for some desired level of minimum detection capability of concentration fluctuations.The methodology is applied in a companion paper (McElroy et al., 1986) to the Las Vegas, Nevada, metropolitan area for the pollutant carbon monoxide.Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency through Contract No. 68-03-2446 to Systems Applications, Inc., it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

Statistical modeling of restricted pollutant data sets to assess compliance with air quality criteria

Three statistical models are used to predict the upper percentiles of the distribution of air pollutant concentrations from restricted data sets recorded over yearly time intervals. The first is an empirical quantile-quantile model. It requires firstly that a more complete date set be available from a base site within the same airshed, and secondly that the base and restricted data sets are drawn from the same distributional form. A two-sided Kolmogorov-Smirnov two-sample test is applied to test the validity of the latter assumption, a test not requiring the assumption of a particular distributional form. The second model represents the a priori selection of a distributional model for the air quality data. To demonstrate this approach the two-parameter lognormal, gamma and Weibull models and the one-parameter exponential model were separately applied to all the restricted data sets. A third model employs a model identification procedure on each data set. It selects the best fit model.     

Water quality assessment using integrated modeling and monitoring in Narva Bay, Gulf of Finland

A coupled three-dimensional hydrodynamic–ecological model was used for the assessment of water quality in Narva Bay during one biologically active season. Narva Bay is located in the south-eastern Gulf of Finland. Narva River with a catchment’s area covering part of Russia and Estonia discharges water and nutrients to Narva Bay. The ecological model includes phytoplankton carbon, nitrogen and phosphorus, chlorophyll a, zooplankton, detritus carbon, nitrogen and phosphorus, inorganic nitrogen, inorganic phosphorus and dissolved oxygen as state variables. Both the hydrodynamic and ecosystem models were validated using a limited number of measurements. The hydrodynamic model validation included comparison of time series of currents and temperature and salinity profiles. The ecological model results were compared with the monitoring data of phytoplankton biomass, total nitrogen and phosphorus and dissolved oxygen. The comparison of hydrodynamic parameters, phytoplankton biomass, surface layer total phosphorus and dissolved oxygen and near-bottom layer total nitrogen was reasonable. Time series of spatially mean values and standard deviations of selected parameters were calculated for the whole Narva Bay. Combining model results and monitoring data, the characteristic concentrations of phytoplankton biomass, total nitrogen and phosphorus and near-bottom dissolved oxygen were estimated. Phytoplankton biomass and total phosphorus showed seasonal variations, of 0.6–1.1 and 0.022–0.032 mg/l, respectively, during spring bloom, 0.1–0.3 and 0.015–0.025 mg/l in summer and 0.2–0.6 and 0.017–0.035 mg/l during autumn bloom. Total nitrogen and near-bottom oxygen concentrations were rather steady, being 0.25–0.35 and 2–6 mg/l, respectively. The total nitrogen and phosphorus concentrations show that according to the classification of Estonian coastal waters, Narva Bay water belongs to a good water quality class.     

Air pollution exposure monitoring and estimating. Part I. Integrated air quality monitoring system

This paper presents an integrated exposure monitoring system, based on an expansion of existing air quality monitoring systems using dispersion modelling. The system allows: (1) identifying geographical areas whose inhabitants are most exposed to ambient pollution; (2) identifying how many people in an area are exposed to concentrations of pollution exceeding air quality guidelines; (3) describing the exposure of population subgroups (e.g. children); (4) planning pollution abatement measures and quantifying their effects; (5) establishing risk assessment and management programs, and (6) investigating the short- and long-term effects of both pollutants and pollution sources on public health. The effect of pollution is rarely very large and in order to discover it, exposure estimation must provide data that reflects both spatial and temporal variations. Estimates of pollution exposure are obtained using an integrated approach that combines results of measurements from monitoring programs with dispersion calculations. These values can serve as estimates for individual short-term or long-term exposure. The grouped data allows the expression of ambient pollution concentrations as the spatial distribution of estimates such as the mean or 98th percentile of such compounds as SO2, O3, NO2, PM10 and PM2.5. This integrated approach has been combined into a single software package, AirQUIS.     

A new protease assay system using gelatin thin film for monitoring indoor air quality

Airborne particulates have a major influence on the quality of indoor environments, which in turn affects human health. Both allergens and proteases are major etiological factors in allergic disease. Thus, the monitoring of environmental protease particulates is very important for the management of allergic disease. Protease-monitoring systems have been introduced in industry, but have not been applied to domestic settings, probably because of the high cost and many complex steps involved in these systems. In this study, we developed an indoor protease-monitoring system which can easily detect protease particulates without need for pre-treatment of dust samples or complex measuring instruments such as fluorescent plate reader. As a protease substrate, gelatin thin film (GTF) was adopted to evaluate small amounts of house dust particulates. The principle of this assay is based on the hydrolysis of GTF. Protease molecules spread from a particulate to GTF can hydrolyze the gelatin, thereby producing a spot on the GTF. This hydrolyzed spot visualized by staining was much larger than the particulate size, making visible to naked eye. The GTF method facilitated the observation of a subtle increase in protease particulates fallen on the GTF after the turnover of a used bed-quilt. The newly developed GTF method seems to be a suitable tool for protease monitoring at home.     

Application of a risk assessment based approach to designing ambient air quality monitoring networks for evaluating non-cancer health impacts

An ambient air quality monitoring network has been establishedusing risk assessment techniques to evaluate adverse health effects from exposures to airborne contaminants. The risk assessment method was compared to traditional methods of establishing air quality monitoring networks: identifying maximumconcentration impacts or maximum total population. Results suggest that the health risk method best predicted the locationof adverse, non-carcinogenic respiratory illnesses during the evaluation period. Spearman Rank Correlation Coefficient, r _s, values obtained using the risk assessment method werestatistically greater than the values obtained using theconcentration and population methods. The concentration methodwas the least accurate predictor of adverse effects.     

Chamber evaluation of a portable GC with tunable retention and microsensor-array detection for indoor air quality monitoring

The evaluation of a novel prototype instrument designed for on-site determinations of VOC mixtures found in indoor working environments is described. The instrument contains a miniature multi-stage preconcentrator, a dual-column separation module with pressure-tunable retention capabilities, and an integrated array of three polymer-coated surface acoustic wave sensors. It was challenged with dynamic test-atmospheres of a set of 15 common indoor air contaminants at parts-per-billion concentrations within a stainless-steel chamber under a range of conditions. Vapours were reliably identified at a known level of confidence by combining column retention times with sensor-array response patterns and applying a multivariate statistical test of pattern fidelity for the chromatographically resolved vapours. Estimates of vapour concentrations fell within 7% on average of those determined by EPA Method TO-17, and limits of detection ranged from 0.2 to 28 ppb at 25 degrees C for 1 L samples collected and analyzed in <12 min. No significant humidity effects were observed (0-90% RH). Increasing the chamber temperature from 25 to 30 degrees C reduced the retention times of the more volatile analytes which, in turn, demanded alterations in the scheduling of column-junction-point pressure (flow) modulations performed during the analysis. Reductions in sensor sensitivities with increasing temperature were predictable and similar among the sensors in the array such that most response patterns were not altered significantly. Short-term fluctuations in concentration were accurately tracked by the instrument. Results indicate that this type of instrument could provide routine, semi-autonomous, near-real-time, multi-vapour monitoring in support of efforts to assess air quality in office environments.     

Indoor air quality modeling for PM10, PM2.5, and PM1.0 in naturally ventilated classrooms of an urban Indian school building

Assessment of indoor air quality (IAQ) in classrooms of school buildings is of prime concern due to its potential effects on student??s health and performance as they spend a substantial amount of their time (6?C7 h per day) in schools. A number of airborne contaminants may be present in urban school environment. However, respirable suspended particulate matter (RSPM) is of great significance as they may significantly affect occupants?? health. The objectives of the present study are twofold, one, to measure the concentrations of PM₁₀ (<10  $\upmu $ m), PM_2.5 (<2.5  $\upmu $ m), and PM_1.0 (<1.0  $\upmu $ m) in naturally ventilated classrooms of a school building located near a heavy-traffic roadway (9,755 and 4,296 vehicles/hour during weekdays and weekends, respectively); and second, to develop single compartment mass balance-based IAQ models for PM₁₀ (NVIAQM_pm10), PM_2.5 (NVIAQM_pm2.5), and PM_1.0 (NVIAQM_pm1.0) for predicting their indoor concentrations. Outdoor RSPM levels and classroom characteristics, such as size, occupancy level, temperature, relative humidity, and CO₂ concentrations have also been monitored during school hours. Predicted indoor PM₁₀ concentrations show poor correlations with observed indoor PM₁₀ concentrations (R ² = 0.028 for weekdays, and 0.47 for weekends). However, a fair degree of agreement (d) has been found between observed and predicted concentrations, i.e., 0.42 for weekdays and 0.59 for weekends. Furthermore, NVIAQM_pm2.5 and NVIAQM_pm1.0 results show good correlations with observed concentrations of PM_2.5 (R ² = 0.87 for weekdays and 0.9 for weekends) and PM_1.0 (R ² = 0.86 for weekdays and 0.87 for weekends). NVIAQM_pm10 shows the tendency to underpredict indoor PM₁₀ concentrations during weekdays as it does not take into account the occupant??s activities and its effects on the indoor concentrations during the class hours. Intense occupant??s activities cause resuspension or delayed deposition of PM₁₀. The model results further suggests conductance of experimental and physical simulation studies on dispersion of particulates indoors to investigate their resuspension and settling behavior due to occupant??s activities/movements. The models have been validated at three different classroom locations of the school site. Sensitivity analysis of the models has been performed by varying the values of mixing factor (k) and newly introduced parameter R _c. The results indicate that the change in values of k (0.33 to 1.00) does not significantly affect the model performance. However, change in value of R _c (0.001 to 0.500) significantly affects the model performance.     

Dynamic extreme values modeling and monitoring by means of sea shores water quality biomarkers and valvometry

Water quality can be evaluated using biomarkers such as tissular enzymatic activities of endemic species. Measurement of molluscs bivalves activity at high frequency (e.g., valvometry) during a long time period is another way to record the animal behavior and to evaluate perturbations of the water quality in real time. As the pollution affects the activity of oysters, we consider the valves opening and closing velocities to monitor the water quality assessment. We propose to model the huge volume of velocity data collected in the framework of valvometry using a new nonparametric extreme values statistical model. The objective is to estimate the tail probabilities and the extreme quantiles of the distribution of valve closing velocity. The tail of the distribution function of valve closing velocity is modeled by a Pareto distribution with parameter ??_t,τ, beyond a threshold τ according to the time t of the experiment. Our modeling approach reveals the dependence between the specific activity of two enzymatic biomarkers (Glutathione-S-transferase and acetylcholinesterase) and the continuous recording of oyster valve velocity, proving the suitability of this tool for water quality assessment. Thus, valvometry allows in real-time in situ analysis of the bivalves behavior and appears as an effective early warning tool in ecological risk assessment and marine environment monitoring.     

Long-term phenol, cresols and BTEX monitoring in urban air

This paper reports the results of a long-term monitoring of benzene, toluene, ethylbenzene, xylenes (BTEX), phenol and cresols in the air of Padua during a wide period of the year 2007 using two radial passive samplers (Radiello system) equipped with BTEX- and phenol-specific cartridges. Two sites were monitored, one in the industrial area and one close to the town centre. Relevant pollution episodes have been observed during both the winter and summer periods. Benzene, together with toluene, ethylbenzene and xylenes showed their maximum concentrations during the winter season, but the secondary pollutant phenol was higher than benzene for a large period of the year when the meteorological conditions blocked the pollutants in the lower layers of the atmosphere and solar radiation increased the benzene photo-oxidation process.     

Microbiological monitoring of air quality in a university canteen: an 11-year report

Over the past decade, an increased tendency to consume meals at dining facilities outside the home has been highlighted; moreover, meals supplied in food businesses have been involved in many foodborne disease outbreaks. Therefore, microbial air contamination in food processing facilities could be a concern and an increase of microbial loads could represent a risk factor, especially for the potential contamination of foods due to undesirable spoiling and pathogenic bacteria. In this paper, the results of an 11-year microbiological monitoring of air quality in a university canteen are reported. The study, which started in the year 2000, was performed within a hazard analysis and critical control point (HACCP) plan implementation of a canteen that produces about 1,000 meals a day in order to verify the effectiveness of corrective actions on the indoor air quality. The primary food preparation room, the kitchen, and three cold rooms underwent air sampling by using a calibrated impaction sampler. Our investigation detected a general and progressive improvement in the air quality of the canteen since the beginning of the study, thus suggesting the appropriateness of the corrective action undertaken during the HACCP implementation program.     

Site location optimization of regional air quality monitoring network in China: methodology and case study

Regional air quality monitoring networks (RAQMN) are urgently needed in China due to increasing regional air pollution in city clusters, arising from rapid economic development in recent decades. This paper proposes a methodological framework for site location optimization in designing a RAQMN adapting to air quality management practice in China. The framework utilizes synthetic assessment concentrations developed from simulated data from a regional air quality model in order to simplify the optimal process and to reduce costs. On the basis of analyzing various constraints such as cost and budget, terrain conditions, administrative district, population density and spatial coverage, the framework takes the maximum approximate degree as an optimization objective to achieve site location optimization of a RAQMN. An expert judgment approach was incorporated into the framework to help adjust initial optimization results in order to make the network more practical and representative. A case study was used to demonstrate the application of the framework, indicating that it is feasible to conduct site optimization for a RAQMN design in China. The effects of different combinations of primary and secondary pollutants on site location optimization were investigated. It is suggested that the network design considering both primary and secondary pollutants could better represent regional pollution characteristics and more extensively reflect temporal and spatial variations of regional air quality. The work shown in this study can be used as a reference to guide site location optimization of a RAQMN design in China or other regions of the world.     

A systems approach to modeling Community-Based Environmental Monitoring: a case of participatory water quality monitoring in rural Mexico

Community-Based Environmental Monitoring (CBM) is a social practice that makes a valuable contribution to environmental management and construction of active societies for sustainable future. However, its documentation and analysis show deficiencies that hinder contrast and comparison of processes and effects. Based on systems approach, this article presents a model of CBM to orient assessment of programs, with heuristic or practical goals. In a focal level, the model comprises three components, the social subject, the object of monitoring, and the means of action, and five processes, data management, social learning, assimilation/decision making, direct action, and linking. Emergent properties were also identified in the focal and suprafocal levels considering community self-organization, response capacity, and autonomy for environmental management. The model was applied to the assessment of a CBM program of water quality implemented in rural areas in Mexico. Attributes and variables (indicators) for components, processes, and emergent properties were selected to measure changes that emerged since the program implementation. The assessment of the first 3 years (2010–2012) detected changes that indicated movement towards the expected results, but it revealed also the need to adjust the intervention strategy and procedures. Components and processes of the model reflected relevant aspects of the CBM in real world. The component called means of action as a key element to transit “from the data to the action.” The CBM model offered a conceptual framework with advantages to understand CBM as a socioecological event and to strengthen its implementation under different conditions and contexts.     

Bioaerosols as contributors to poor air quality in Taichung City, Taiwan

Bioaerosols are a type of suspended sediments that contribute to poor air quality in Taiwan. Bioaerosols include allergens such as: fungi, bacteria, actinomycetes, arthropods and protozoa, as well as microbial products such as mycotoxins, endotoxins and glucans. When allergens and microbial products are suspended in the air, local air quality will be influenced adversely. In addition, when the particle size is small enough to pass through the respiratory tract entering the human body, the health of the local population is also threatened. Therefore, the purpose of this study is to attempt to understand the concentration and types of bacteria and the bacteria numbers for various particle size ranges during a study period of June 2005 to February 2006 in Taichung City, Central Taiwan. The results indicate that the total average bacterial concentration by using NA medium incubated for 48 h were 8.0 × 10², 1.4 × 10³, 2.4 × 10³ and 1.3 × 10³, 1.9 × 10³, 3.5 × 10³ cfu/m³ for CMES, TRIPS and RFS sampling sites during the daytime and nighttime period of June 2005 to February 2006. Moreover, the total average bacterial concentration by using R₂A medium incubated for 48 h were 8.5 × 10², 1.5 × 10³, 2.2 × 10³ and 1.2 × 10³, 1.7 × 10³, 2.5 × 10³ cfu/m³ for CMES, TRIPS and RFS sampling sites the daytime and nighttime during the same sampling period. The total average bacteria concentration was the same in either NA or R₂A medium for the same sampling times or sites. The total average bacterial concentration was higher in daytime period than that of nighttime period for CMES, TRIPS and RFS sampling sites. The high average bacterial concentration was found in the particle size range of 0.53–0.71 mm (average bioaerosol size was in the range of 2.1–4.7 μm) for each sampling site. Also, 20 kinds of bacteria exceeded levels for each sampling site and were classified as according to shape: rod, coccus and filamentous.     

The design of long-term air quality monitoring networks in urban areas using a spatiotemporal approach

The implementation and maintenance of an air pollution monitoring program can be expensive and time consuming, especially when the aim is for long-term monitoring over a significant area. Consequently, it is essential that sites are optimized to provide the best representative cover while minimizing costs. In the past, there has been a tendency to locate sampling stations at pollution hot-spots. While this is acceptable for determining a maximum potential exposure or identifying the extent of a risk, there are limitations to this approach when assessing the potential impact of any future abatement strategies or determining the level of exposure outside the vicinity. This paper presents an approach in which representative air quality assessments can be undertaken for an urban area using the minimum number of measurement sites. A novel methodology is described that involves site selection to capture the maximum variance in measured pollutants, while minimizing spatiotemporal autocorrelation between the selected sites. A case study is presented for Yazd, Iran. Overall, the results show that the proposed methodology can be effective and enable the long-term monitoring of air pollution to be undertaken on a cost-effective basis in urban areas. In addition, there is the potential for the methodology to be utilized for other forms of pollution (e.g., water, soil, and noise).     

Industrial effluent quality, pollution monitoring and environmental management

Royal Commission Environmental Control Department (RC-ECD) at Yanbu industrial city in Kingdom of Saudi Arabia has established a well-defined monitoring program to control the pollution from industrial effluents. The quality of effluent from each facility is monitored round the clock. Different strategic measures have been taken by the RC-ECD to implement the zero discharge policy of RC. Industries are required to pre-treat the effluent to conform pretreatment standards before discharging to central biological treatment plant. Industries are not allowed to discharge any treated or untreated effluent in open channels. After treatment, reclaimed water must have to comply with direct discharge standards before discharge to the sea. Data of industrial wastewater collected from five major industries and central industrial wastewater treatment plant (IWTP) is summarized in this report. During 5-year period, 3,705 samples were collected and analyzed for 43,436 parameters. There were 1,377 violations from pretreatment standards from all the industries. Overall violation percentage was 3.17%. Maximum violations were recorded from one of the petrochemical plants. The results show no significant pollution due to heavy metals. Almost all heavy metals were within RC pretreatment standards. High COD and TOC indicates that major pollution was due to hydrocarbons. Typical compounds identified by GC-MS were branched alkanes, branched alkenes, aliphatic ketones, substituted thiophenes, substituted phenols, aromatics and aromatic alcohols. Quality of treated water was also in compliance with RC direct discharge standards. In order to achieve the zero discharge goal, further studies and measures are in progress.