Industrial Zoning as a Means of Controlling Area Source Air Pollution

The simple ATDL urban dispersion model Is based on the formula Xo(g/m³) = CO(g/m²s)/U(m/s). The diurnal variation of the stability factor C, which can be thought of as the width of the urban area divided by the vertical dispersion of the pollution cloud, has not before been satisfactorily estimated. Using observed diurnal variations of CO concentrations and traffic frequencies reported by DeMarrais of the EPA for many stations in the states of Maryland, New Jersey, and Colorado, and using wind data from these states, hourly values of C - XoU/Q were calculated. The ratio of C to the daily average C is found to equal about 2.5 at 4 a.m., drops to about 0.5 by 8 a.m., and remains at 0.5 until about 6 p.m.., when it starts to climb slowly again. Application of this new stability factor to independent CO data from Los Angeles yields correlations between measured and predicted concentrations of about 0.7.     

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NO_X), carbon monoxide (CO), fine particulate matter (PM₁₀) and sulfur dioxide (SO₂)) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement d values reaching 0.58, fractional bias “FB” and geometric mean bias “MG” values approaching 0 and 1, respectively, and normalized mean square error “NMSE” values as low as 2.17. However, median ratios of predicted to observed concentrations “Cp/Co” at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NO_X, CO, PM₁₀ and SO₂, respectively, and the fraction of predictions within a factor of two of observations “FAC2” values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.

Implications In the absence of site-specific source emission factors, the use of internationally reported emission factors without testing their validity may generate significant errors. Instead, recorded field measurements and meteorological data may be combined with atmospheric transport and dispersion models to better estimate source emissions, particularly in regulatory compliance studies. In this context, lower model performance is expected at higher wind speeds for most indicators such as CO, PM₁₀, and SO₂.     

A measurement campaign in a street canyon in Helsinki and comparison of results with predictions of the OSPM model

In 1997, a measuring campaign was conducted in a street canyon (Runeberg St.) in Helsinki. Hourly mean concentrations of CO, NO_x, NO₂ and O₃ were measured at street and roof levels, the latter in order to determine the urban background concentrations. The relevant hourly meteorological parameters were measured at roof level; these included wind speed and direction, temperature and solar radiation. Hourly street level measurements and on-site electronic traffic counts were conducted throughout the whole of 1997; roof level measurements were conducted for approximately two months, from 3 March to 30 April in 1997. CO and NO_x emissions from traffic were computed using measured hourly traffic volumes and evaluated emission factors. The Operational Street Pollution Model (OSPM) was used to calculate the street concentrations and the results were compared with the measurements. The overall agreement between measured and predicted concentrations was good for CO and NO_x (fractional bias were −4.2 and +4.5%, respectively), but the model overpredicted the measured NO₂ concentrations (fractional bias was +22%). The agreement between the measured and predicted values was also analysed in terms of its dependence on wind speed and direction; the latter analysis was performed separately for two categories of wind velocity. The model qualitatively reproduces the observed behaviour very well. The database, which contains all measured and predicted data, is available for further testing of other street canyon dispersion models. The dataset contains a larger proportion of low wind speed cases, compared with other available street canyon measurement datasets.     

The In-Plume Emission Test Stand: An Instrument Platform for the Real-Time Characterization of Fuel-Based Combustion Emissions

Abstract

The In-Plume Emission Test Stand (IPETS) characterizes gaseous and particulate matter (PM) emissions from combustion sources in real time. Carbon dioxide (CO₂), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂), and other gases are quantified with a closed-path Fourier transform infrared spectrometer (FTIR). Particle concentrations, chemical composition, and other particle properties are characterized with an electrical low-pressure impactor (ELPI), a light-scattering particle detector, an optical particle counter, and filter samples amenable to different laboratory analysis. IPETS measurements of fuel-based emission factors for a diesel generator are compared with those from a Mobile Emissions Laboratory (MEL). IPETS emission factors ranged from 0.3 to 11.8, 0.2 to 3.7, and 22.2 to 32.8 g/kg fuel for CO, NO₂, and NO, respectively. IPETS PM emission factors ranged from 0.4 to 1.4, 0.3 to 1.8, 0.3 to 2.2, and 1 to 3.4 g/kg fuel for filter, photoacoustic, nephelometer, and impactor measurements, respectively. Observed linear regression statistics for IPETS versus MEL concentrations were as follows: CO slope = 1.1, r² = 0.99; NO slope = 1.1, r² = 0.92; and NO₂ slope = 0.8, r² = 0.96. IPETS versus MEL PM regression statistics were: filter slope = 1.3, r² = 0.80; ELPI slope = 1.7, r² = 0.87; light-scattering slope = 2.7, r² = 0.92; and photoacoustic slope = 2.1, r² = 0.91. Lower temperatures in the dilution air (~25 °C for IPETS vs. ~50 °C for MEL) may result in greater condensation of semi-volatile compounds on existing particles, thereby explaining the 30% difference for filters. The other PM measurement devices are highly correlated with the filter, but their factory-default PM calibration factors do not represent the size and optical properties of diesel exhaust. They must be normalized to a simultaneous filter measurement.     

Statistical and diagnostic evaluation of a new-generation urban dispersion modelling system against an extensive dataset in the Helsinki area

We have developed a modelling system for predicting the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. This paper describes a comparison of the NO_x and NO₂ concentrations predicted using this modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations at four permanently located and three mobile monitoring stations in the Helsinki Metropolitan Area in 1996–1997 (at a total of ten urban and suburban measurement locations). At the stations considered, the so-called index of agreement values of the predicted and measured time series of the NO₂ concentrations vary between 0.65 and 0.82, while the fractional bias values range from −0.29 to +0.26. In comparison with corresponding results presented in the literature, the agreement between the measured and predicted datasets is good, as indicated by these statistical parameters. The seasonal variations of the NO₂ concentrations were analysed in terms of the relevant meteorological parameters. We also analysed the difference between model predictions and measured data diagnostically, in terms of meteorological parameters, including wind speed and direction (the latter separately for two wind speed classes), atmospheric stability and ambient temperature, at two monitoring stations in central Helsinki. The modelling system tends to overpredict the measured NO₂ concentrations both at the highest (u⩾6 m s⁻¹) and at the lowest wind speeds (u<2 m s⁻¹). For higher wind speeds, the modelling system overpredicts the measured NO₂ concentrations in certain wind direction intervals; specific ranges were found for both monitoring stations considered. The modelling system tends to underpredict the measured concentrations in convective atmospheric conditions, and overpredict in stable conditions. The possible physico-chemical reasons for these differences are discussed.     

Odor dispersion modeling with CALPUFF: Case study of a waste and residue treatment incineration and utilization plant in Kocaeli,Turkey

This study addresses the odor problem at a waste and residue treatment incineration and utilization plant located within the borders the Alikahya district of the Kocaeli province in Turkey. In the first stage of the study, odor measurements were made at designated sampling points, while in the second stage, odor concentrations were determined at the receptor points through dispersion modelling using a USEPA (United States Environmental Protection Agency) certified long-range (>50 km) CALPUFF lagrangian puff model. In the final stage, an analysis of the predicted and observed values was carried out using such statistical methods as geometric mean bias (MG), geometric variance (VG) and fractions of predictions within a factor of two observations (FAC2).

During the modelling study, the highest one-hour concentration level was found to be 1868.10 OU (Odor Units), and the 24-hour concentration level was found to be 1316 OU, representing a decrease of approximately 30 percent. According to the measurement made, the maximum concentration value was 2455 OU. Odor measurements were carried out at 13 points within the area in order to assess the prediction results. When the results were assessed using the MG, VG and FAC2 statistical methods, it was observed that an acceptable model performance was not achieved for the whole sampling point. When the reason for this was investigated, it was concluded that the observed values were lower than the predicted values due to the fact on the measurement days, the odor was dispersed by wind. It was further concluded that the observed values were higher than the predicted values as a result of odors emitted by other plants in the area. When the measurements in residential areas were examined to identify the effect of the odors, it was determined that although the primary density of settlement is to the southwest of the plant, it was not this area that was affected most, but rather the area to the northeast, where there is a lower settlement density.     

Evaluation of a novel high throughput screening tool for relative emissions of industrial chemicals used in chemical products

Tens of thousands of chemicals are currently marketed worldwide, but only a small number of these compounds has been measured in effluents or the environment to date. The need for screening methodologies to select candidates for environmental monitoring is therefore significant. To meet this need, the Swedish Chemicals Agency developed the Exposure Index (EI), a model for ranking emissions to a number of environmental matrices based on chemical quantity used and use pattern. Here we evaluate the EI. Data on measured concentrations of organic chemicals in sewage treatment plants, one of the recipients considered in the EI model, were compiled from the literature, and the correlation between predicted emission levels and observed concentrations was assessed by linear regression analysis. The adequacy of the parameters employed in the EI was further explored by calibration of the model to measured concentrations. The EI was found to be of limited use for ranking contaminant levels in STPs; the r² values for the regressions between predicted and observed values ranged from 0.02 (p = 0.243) to 0.14 (p = 0.007) depending on the dataset. The calibrated version of the model produced only slightly better predictions although it was fitted to the experimental data. However, the model is a valuable first step in developing a high throughput screening tool for organic contaminants, and there is potential for improving the EI algorithm.     

Biomass smoke in Burkina Faso: what is the relationship between particulate matter,carbon monoxide,and kitchen characteristics?

In Burkina Faso where cooking with biomass is very common, little information exists regarding kitchen characteristics and their impact on air pollutant levels. The measurement of air pollutants such as respirable particulate matter (PM₁₀), an important component of biomass smoke that has been linked to adverse health outcomes, can also pose challenges in terms of cost and the type of equipment needed. Carbon monoxide could potentially be a more economical and simpler measure of air pollution. The focus of this study was to first assess the association of kitchen characteristics with measured PM₁₀ and CO levels and second, the relationship of PM₁₀ with CO concentrations, across these different kitchen characteristics in households in Nouna, Burkina Faso. Twenty-four-hour concentrations of PM₁₀ (area) were measured with portable monitors and CO (area and personal) estimated using color dosimeter tubes. Data on kitchen characteristics were collected through surveys. Most households used both wood and charcoal burned in three-stone and charcoal stoves. Mean outdoor kitchen PM₁₀ levels were relatively high (774 μg/m³, 95 % CI 329–1,218 μg/m³), but lower than indoor concentrations (Satterthwaite t value, ?6.14; p?<?0.0001). In multivariable analyses, outdoor kitchens were negatively associated with PM₁₀ (OR?=?0.06, 95 % CI 0.02–0.16, p value <0.0001) and CO (OR?=?0.03, 95 % CI 0.01–0.11, p value <0.0001) concentrations. Strong area PM₁₀ and area CO correlations were found with indoor kitchens (Spearman’s r?=?0.82, p?<?0.0001), indoor stove use (Spearman’s r?=?0.82, p?<?0.0001), and the presence of a smoker in the household (Spearman’s r?=?0.83, p?<?0.0001). Weak correlations between area PM₁₀ and personal CO levels were observed with three-stone (Spearman’s r?=?0.23, p?=?0.008) and improved stoves (Spearman’s r?=?0.34, p?=?0.003). This indicates that the extensive use of biomass fuels and multiple stove types for cooking still produce relatively high levels of exposure, even outdoors, suggesting that both fuel subsidies and stove improvement programs are likely necessary to address this problem. These findings also indicate that area CO color dosimeter tubes could be a useful measure of area PM₁₀ concentrations when levels are influenced by strong emission sources or when used in indoors. The weaker correlation observed between area PM₁₀ and personal CO levels suggests that area exposures are not as useful as proxies for personal exposures, which can vary widely from those recorded by stationary monitors.     

Simulating industrial emissions using atmospheric dispersion modeling system: model performance and source emission factors

In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NOx), carbon monoxide (CO), fine particulate matter (PM10) and sulfur dioxide (SO2)) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement 'd' values reaching 0.58, fractional bias 'FB' and geometric mean bias 'MG' values approaching 0 and 1, respectively, and normalized mean square error 'NMSE' values as low as 2.17. However, median ratios of predicted to observed concentrations 'Cp/Co' at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NOx, CO, PM10 and SO2, respectively, and the fraction of predictions within a factor of two of observations 'FAC2' values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.     

Improving the indoor air quality of respiratory type of medical facility by zeolite filtering

This study investigated the indoor air quality (IAQ) conditions of carbon dioxide (CO₂), carbon monoxide (CO), ozone (O₃), formaldehyde (HCHO), total volatile organic compounds (TVOCs), and bio-aerosols (bacteria and fungi) in a respiratory type of medical facility in Chia-Yi County in southern Taiwan. Among those IAQ conditions, the concentrations of CO, O₃, and HCHO exceeded the regulation values of the Taiwan Environmental Protection Administration (EPA) mostly in the morning. The concentrations of bacteria and fungi did not exceed the regulation values but still posed potential health and environment problems for workers, patients, and visitors. Therefore, self-made silver-coated zeolite (AgZ) was used as a filter material in air cleaners to remove bio-aerosols in the respiratory care ward (RCW), and the removals were still effective after 120 hr. The cumulative bio-aerosol removals for bacteria and fungi were 900 and 1,088 colony-forming units (CFU) g^?1 after 24 hr and were above 3,100 and 2,700 CFU g^?1 after 120 hr. From the research results, it is suggested that AgZ filtering could be used as a feasible engineering measure for hospitals to control their bacteria and fungi parameters in IAQ management. Hospitals should maintain their environmental management and monitoring programs and use different engineering measures to improve different IAQ parameters.

Implications: This study investigated the IAQ conditions in the field at a hospital in Chia-Yi County in southern Taiwan. Although concentrations of most parameters were still within the regulation values, the concentrations of CO, O₃, and HCHO were partially exceeded. We propose a method using an air cleaner with silver-coated zeolite (AgZ) as a possible engineering measure, and there were effective reductions of bacteria and fungi to lower levels with antibacterial effects after 120 hr. Furthermore, this study implies that hospitals should continuously maintain environmental monitoring programs and adopt optimal engineering measures for different needs.     

Emissions into the Atmosphere Due to the Ceramic Salt Glazing Process

The techniques of Principal Component Analysis (PCA) and subsequent regression analysis were used in an attempt to describe local and upwind chemical and physical factors which affect the variability of SO₄ ^–2 concentrations observed in a rural area of the northeastern U.S. The data used in the analyses included upwind and local O₃ concentrations, temperature, relative humidity and other climatological information, SO₂, and meteorological information associated with backward trajectories. The investigation identified five principal components, three major (eigenvalues >1) and two minor (eigenvalues < one), which accounted for 52% (r = 0.72) of the variability in the SO₄ ^–2 regression model. These components can be described as representing local and upwind photochemistry, droplet growth, SO₂ emissions, and air mass characteristics. The study also indicated that in future studies it will be necessary to a priori select air pollution and meteorological variables for measurement to potentially increase the sensitivity of this type of receptor model.     

Bioaccumulation Kinetics and Bioconcentration Factor of Chlorinated Pesticides in Tissues of Puntius ticto (Ham.)

Abstract

Bioaccumulation kinetics and bioconcentration factor (BCF) of chlorinated pesticides like Aldrin, Dieldrin, Benzene hexachloride (BHC), and Dichloro-diphenyl-dichloro-ethane (DDT) in fish tissues of Puntius ticto was studied in detail in a continuous fed system. The bioconcentration process is summarized by using a first order uptake model and the steady-state BCF is calculated based on the 30 days exposure. Rate of bioaccumulation of DDT was maximum of 4.6432 µg g^?1 wet weight per day in liver tissue whereas it was minimum of 0.0002 µg g^?1 wet weight per day in case of Dieldrin in the muscle tissue among the pesticides. It was observed that DDT showed maximum BCF of 89,010 in case of liver tissue of the fish exposed to 30 days. The regression coefficient (r ²) between pesticide concentration and exposure time varied between 0.6212 and 0.9817 indicating high correlation. Based on actual calculated BCF values, the octanol–water partition coefficient (K _ow) values were predicted. In order to prove the hydrophobic property of chlorinated compounds and its affinity towards lipid, the K _ow is predicted. Results showed that pesticide burden differ from tissue to tissue and can be correlated to the lipid content, size, exposure time, and species.     

Performance evaluation of the portable MiniVOL particulate matter sampler

The MiniVOL sampler is a popular choice for use in air quality assessments because it is portable and inexpensive relative to fixed site monitors. However, little data exist on the performance characteristics of the sampler. The reliability, precision, and comparability of the portable MiniVOL PM₁₀ and PM_2.5 sampler under typical ambient conditions are described in this paper. Results indicate that the MiniVOL (a) operated reliably and (b) yielded statistically similar concentration measurements when co-located with another MiniVOL (r²=0.96 for PM₁₀ measurements and r²=0.95 for PM_2.5 measurements). Thus, the characterization of spatial distributions of PM₁₀ and PM_2.5 mass concentrations with the MiniVOL can be accomplished with a high level of confidence. The MiniVOL also produced statistically comparable results when co-located with a Dichotomous Sampler (r²=0.83 for PM₁₀ measurements and r²=0.85 for PM_2.5 measurements) and a continuous mass sampling system (r²=0.90 for PM₁₀ measurements). Environmental factors such as ambient concentration, wind speed, temperature, and humidity may influence the relative measurement comparability between these sampling systems.     

Radical precursors and related species from traffic as observed and modeled at an urban highway junction

Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals and are believed to favor ozone formation significantly. Traffic emission data for both compounds are scarce and mostly outdated. A better knowledge of today's HCHO and HONO emissions related to traffic is needed to refine air quality models. Here the authors report results from continuous ambient air measurements taken at a highway junction in Houston, Texas, from July 15 to October 15, 2009. The observational data were compared with emission estimates from currently available mobile emission models (MOBILE6; MOVES [MOtor Vehicle Emission Simulator]). Observations indicated a molar carbon monoxide (CO) versus nitrogen oxides (NO_x) ratio of 6.01 ± 0.15 (r ² = 0.91), which is in agreement with other field studies. Both MOBILE6 and MOVES overestimate this emission ratio by 92% and 24%, respectively. For HCHO/CO, an overall slope of 3.14 ± 0.14 g HCHO/kg CO was observed. Whereas MOBILE6 largely underestimates this ratio by 77%, MOVES calculates somewhat higher HCHO/CO ratios (1.87) than MOBILE6, but is still significantly lower than the observed ratio. MOVES shows high HCHO/CO ratios during the early morning hours due to heavy-duty diesel off-network emissions. The differences of the modeled CO/NO_x and HCHO/CO ratios are largely due to higher NO_x and HCHO emissions in MOVES (30% and 57%, respectively, increased from MOBILE6 for 2009), as CO emissions were about the same in both models. The observed HONO/NO_x emission ratio is around 0.017 ± 0.0009 kg HONO/kg NO_x which is twice as high as in MOVES. The observed NO₂/NO_x emission ratio is around 0.16 ± 0.01 kg NO₂/kg NO_x, which is a bit more than 50% higher than in MOVES. MOVES overestimates the CO/CO₂ emission ratio by a factor of 3 compared with the observations, which is 0.0033 ± 0.0002 kg CO/kg CO₂. This as well as CO/NO_x overestimation is coming from light-duty gasoline vehicles.

Implications: Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals that ultimately contribute to ozone formation. There still exist uncertainties in emission sources of HONO and HCHO and thus regional air quality modeling still tend to underestimate concentrations of free radicals in the atmosphere. This paper demonstrates that the latest U.S. Environmental Protection Agency (EPA) traffic emission model MOVES still shows significant deviations from observed emission ratios, in particular underestimation of HCHO/CO and HONO/NO_x ratios. Improving the performance of MOVES may improve regional air quality modeling.     

Modeling traffic air pollution in street canyons in New York City for intra-urban exposure assessment in the US Multi-Ethnic Study of atherosclerosis and air pollution

We evaluated the Danish AirGIS air quality and exposure model system using air quality measurement data from New York City in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Measurements were used from three US EPA Air Quality System (AQS) monitoring stations and a comprehensive MESA Air measurement campaign including about 150 different locations and about 650 samples of about 2 week measurements of NO_x, NO₂ and PM_2.5. AirGIS is a deterministic exposure model system based on the dispersion models Operational Street Pollution Model (OSPM) and the Urban Background Model (UBM). The UBM model reproduced the annual levels within 1–26% depending on station and pollutant at the three urban background EPA monitor stations, and generally reproduced well the seasonal and diurnal variation. The full model with OSPM and UBM reproduced the MESA Air measurements with a correlation coefficient of r² = 0.51 for NO_x, r² = 0.28 for NO₂ and r² = 0.73 for PM_2.5.     

Assessment of traffic-related air pollution in the urban streets before and during the 2008 Beijing Olympic Games traffic control period

In order to investigate the air quality and the abatement of traffic-related pollution during the 2008 Olympic Games, we select 12 avenues in the urban area of Beijing to calculate the concentrations of PM₁₀, CO, NO₂ and O₃ before and during the Olympic traffic controlling days, with the OSPM model.Through comparing the modeled results with the measurement results on a representative street, the OSPM model is validated as sufficient to predict the average concentrations of these pollutants at street level, and also reflects their daily variations well, i.e. CO presents the similar double peaks as the traffic flow, PM₁₀ concentration is influenced by other sources. Meanwhile, the model predicts O₃ to stay less during the daytime and ascend in the night, just opposite to NO₂, which reveals the impact of photochemical reactions. In addition, the predicted concentrations on the windward side often exceed the leeward side, indicating the impact of the special street shape, as well as the wind.The comparison between the predicted street concentrations before and during the Olympic traffic control period shows that the overall on-road air quality was improved effectively, due to the 32.3% traffic flow reduction. The concentrations of PM₁₀, CO and NO₂ have reduced from 142.6 μg m⁻³, 3.02 mg m⁻³ and 118.7 μg m⁻³ to 102.0 μg m⁻³, 2.43 mg m⁻³ and 104.1 μg m⁻³. However, the different pollutants show diverse changes after the traffic control. PM₁₀ decreases most, and the reduction effect focusing on the first half-day even clears the morning peak, whereas CO and NO₂ have even reductions to minify the daily fluctuations on the whole. Opposite to the other pollutants, ozone shows an increase of concentration. The average reduction rate of PM₁₀, CO, NO₂ and O₃ are respectively 28%, 19.3%, 12.3% and −25.2%. Furthermore, the streets in east, west, south and north areas present different air quality improvements, probably induced by the varied background pollution in different regions around Beijing, along with the impact of wind force. This finding suggests the pollution control in the surrounding regions, not only in the urban area.     

Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis

This study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM₁₀), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM_2.5) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM_2.5 (r² = 0.62), and the two-variable (AOD-PM_2.5) model predicted PM_2.5 (r² = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM_2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM_2.5. For the relevant period in 2008, in Roda, VA, the predicted PM_2.5 mass concentration is 9.11 ± 5.16 μg m^-3 (mean ± 1SD).

Implications: This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or “hollows,” where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.     

Measurement of total OH reactivity by laser-induced pump and probe technique—comprehensive observations in the urban atmosphere of Tokyo

Total OH reactivity was observed by use of the laser-induced pump and probe technique, and the urban air quality in Tokyo was diagnosed comprehensively. The concentrations of NO_x, CO, O₃, non-methane hydrocarbons (NMHCs) and oxygenated volatile organic compounds (OVOCs) were observed simultaneously. The observations were conducted in July and August 2003, and in January, February, May, and November 2004. Generally, the observed OH reactivity was higher than the calculated values derived using the observed concentrations of the trace species. The differences between the observed and calculated values in summer, spring, and autumn were approximately 30%. However, the difference in winter was smaller than those in the other seasons. In addition, while the differences observed in summer, spring, and autumn correlated with the total reactivity of the OVOCs (Σ_i k_OVOCi[OVOC_i](s⁻¹), k_i is rate constant of its compounds with OH), the correlations were not confirmed in the case of winter because atmospheric oxidation was less active and OVOCs levels were low in winter. These results suggest that the secondary products of the photochemical reactions in the atmosphere would be a missing sink for the OH loss process in the urban area.     

Develop dynamic model for predicting traffic CO emissions in urban areas

The greater the use of energy in the transportation sectors, the higher the emission of carbon monoxide (CO), and hence inevitable harm to environment and human health. In this concern, measuring and predicting of CO emission from transportation sector—especially large cities—is important as it constitute 90 % of all CO emission. Many urban cities in developing world have not properly experienced such measurements or predictions. In this paper, for the first time, field measurements of traffic characteristics data and corresponding CO concentration have been performed for developing a model for predicting CO emissions from transportation sector for New Borg El Arab (NBC), Egypt. The performance of Swiss-German Handbook Emission Factors for Road Transport (HBEFA v3.1) model has been assessed for predicting the CO concentration at roadside in the study area. Results indicated that HBEFA v3.1 underestimate emission figures. The developed CO dynamic emission model involves the traffic flow characteristics with roadside CO concentrations. Acceptable representation of measured CO concentration has been shown by the developed dynamic CO emission model which introduces R ²?=?0.77, mean biases and frictional biases of ?0.27 mg m^?3 and 0.09, respectively. A comparison between predicted CO concentrations using HBEFA v3.1 and the promoted dynamic model indicate that HBEFA v3.1 estimates CO emission concentrations in the study area with a mean error and frictional biases 159.26 and 233.33 %, respectively, higher than those of the developed model.     

Using Continuous PM2.5 Monitoring Data to Report an Air Quality Index

Abstract

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM_2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area’s (MSA’s) AQI.¹ Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality.

This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM_2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency’s (EPA’s) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity.² The chosen measure of model adequacy is r², the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greens-boro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r² of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r² of 0.86, based on 214 paired observations.