Public health and components of particulate matter: The changing assessment of black carbon

In 2012, the WHO classified diesel emissions as carcinogenic, and its European branch suggested creating a public health standard for airborne black carbon (BC). In 2011, EU researchers found that life expectancy could be extended four to nine times by reducing a unit of BC, vs reducing a unit of PM_2.5. Only recently could such determinations be made. Steady improvements in research methodologies now enable such judgments.

In this Critical Review, we survey epidemiological and toxicological literature regarding carbonaceous combustion emissions, as research methodologies improved over time. Initially, we focus on studies of BC, diesel, and traffic emissions in the Western countries (where daily urban BC emissions are mainly from diesels). We examine effects of other carbonaceous emissions, e.g., residential burning of biomass and coal without controls, mainly in developing countries.

Throughout the 1990s, air pollution epidemiology studies rarely included species not routinely monitored. As additional PM_2.5. chemical species, including carbonaceous species, became more widely available after 1999, they were gradually included in epidemiological studies. Pollutant species concentrations which more accurately reflected subject exposure also improved models.

Natural “interventions” - reductions in emissions concurrent with fuel changes or increased combustion efficiency; introduction of ventilation in highway tunnels; implementation of electronic toll payment systems – demonstrated health benefits of reducing specific carbon emissions. Toxicology studies provided plausible biological mechanisms by which different PM species, e.g., carbonaceous species, may cause harm, aiding interpretation of epidemiological studies.

Our review finds that BC from various sources appears to be causally involved in all-cause, lung cancer, and cardiovascular mortality, morbidity, and perhaps adverse birth and nervous system effects. We recommend that the U.S. EPA rubric for judging possible causality of PM_2.5. mass concentrations, be used to assess which PM_2.5. species are most harmful to public health.

Implications: Black carbon (BC) and correlated co-emissions appear causally related with all-cause, cardiovascular, and lung cancer mortality, and perhaps with adverse birth outcomes and central nervous system effects. Such findings are recent, since widespread monitoring for BC is also recent. Helpful epidemiological advances (using many health relevant PM_2.5 species in models; using better measurements of subject exposure) have also occurred. “Natural intervention” studies also demonstrate harm from partly combusted carbonaceous emissions. Toxicology studies consistently find biological mechanisms explaining how such emissions can cause these adverse outcomes. A consistent mechanism for judging causality for different PM_2.5 species is suggested.

A list of acronyms will be found at the end of the article.     

Translating landfill methane generation parameters among first-order decay models

Landfill gas (LFG) generation is predicted by a first-order decay (FOD) equation that incorporates two parameters: a methane generation potential (L₀) and a methane generation rate (k). Because non-hazardous waste landfills may accept many types of waste streams, multiphase models have been developed in an attempt to more accurately predict methane generation from heterogeneous waste streams. The ability of a single-phase FOD model to predict methane generation using weighted-average methane generation parameters and tonnages translated from multiphase models was assessed in two exercises. In the first exercise, waste composition from four Danish landfills represented by low-biodegradable waste streams was modeled in the Afvalzorg Multiphase Model and methane generation was compared to the single-phase Intergovernmental Panel on Climate Change (IPCC) Waste Model and LandGEM. In the second exercise, waste composition represented by IPCC waste components was modeled in the multiphase IPCC and compared to single-phase LandGEM and Australia’s Solid Waste Calculator (SWC). In both cases, weight-averaging of methane generation parameters from waste composition data in single-phase models was effective in predicting cumulative methane generation from -7% to +6% of the multiphase models. The results underscore the understanding that multiphase models will not necessarily improve LFG generation prediction because the uncertainty of the method rests largely within the input parameters. A unique method of calculating the methane generation rate constant by mass of anaerobically degradable carbon was presented (k_c) and compared to existing methods, providing a better fit in 3 of 8 scenarios. Generally, single phase models with weighted-average inputs can accurately predict methane generation from multiple waste streams with varied characteristics; weighted averages should therefore be used instead of regional default values when comparing models.

Implications: Translating multiphase first-order decay model input parameters by weighted average shows that single-phase models can predict cumulative methane generation within the level of uncertainty of many of the input parameters as defined by the Intergovernmental Panel on Climate Change (IPCC), which indicates that decreasing the uncertainty of the input parameters will make the model more accurate rather than adding multiple phases or input parameters.     

Statistical analysis of winter ozone exceedances in the Uintah Basin,Utah, USA

Because of the confluence of several factors (persistent multiday inversions, petroleum production, and snow cover), the Uintah Basin of eastern Utah, USA, exhibits high concentrations of winter ozone. A regression analysis is presented that successfully predicts daily ozone concentration with a standard error of about 11 ppb. It also predicts with 90% accuracy whether any given day will exceed the National Ambient Air Quality Standard for ozone, 70 ppb. An analysis is introduced for calculating a “pseudo-lapse rate,” a determination of inversion intensity in the absence of sounding data. By combining the model with historical meteorological data, it is possible to make long-range predictions about ozone formation. The odds of observing no exceedance days in any given season are 38%. The odds of only three or fewer exceedance days in any given season are 46%.

Implications: This paper provides an improved understanding of the scientific underpinnings of the winter ozone phenomenon and an ability to make long-range predictions.     

Application of an integrated Weather Research and Forecasting (WRF)/CALPUFF modeling tool for source apportionment of atmospheric pollutants for air quality management: A case study in the urban area of Benxi,China

In this study, the authors endeavored to develop an effective framework for improving local urban air quality on meso-micro scales in cities in China that are experiencing rapid urbanization. Within this framework, the integrated Weather Research and Forecasting (WRF)/CALPUFF modeling system was applied to simulate the concentration distributions of typical pollutants (particulate matter with an aerodynamic diameter <10 μm [PM₁₀], sulfur dioxide [SO₂], and nitrogen oxides [NO_x]) in the urban area of Benxi. Statistical analyses were performed to verify the credibility of this simulation, including the meteorological fields and concentration fields. The sources were then categorized using two different classification methods (the district-based and type-based methods), and the contributions to the pollutant concentrations from each source category were computed to provide a basis for appropriate control measures. The statistical indexes showed that CALMET had sufficient ability to predict the meteorological conditions, such as the wind fields and temperatures, which provided meteorological data for the subsequent CALPUFF run. The simulated concentrations from CALPUFF showed considerable agreement with the observed values but were generally underestimated. The spatial-temporal concentration pattern revealed that the maximum concentrations tended to appear in the urban centers and during the winter. In terms of their contributions to pollutant concentrations, the districts of Xihu, Pingshan, and Mingshan all affected the urban air quality to different degrees. According to the type-based classification, which categorized the pollution sources as belonging to the Bengang Group, large point sources, small point sources, and area sources, the source apportionment showed that the Bengang Group, the large point sources, and the area sources had considerable impacts on urban air quality. Finally, combined with the industrial characteristics, detailed control measures were proposed with which local policy makers could improve the urban air quality in Benxi. In summary, the results of this study showed that this framework has credibility for effectively improving urban air quality, based on the source apportionment of atmospheric pollutants.

Implications: The authors endeavored to build up an effective framework based on the integrated WRF/CALPUFF to improve the air quality in many cities on meso-micro scales in China. Via this framework, the integrated modeling tool is accurately used to study the characteristics of meteorological fields, concentration fields, and source apportionments of pollutants in target area. The impacts of classified sources on air quality together with the industrial characteristics can provide more effective control measures for improving air quality.

Through the case study, the technical framework developed in this study, particularly the source apportionment, could provide important data and technical support for policy makers to assess air pollution on the scale of a city in China or even the world.     

The study on biomass fraction estimate methodology of municipal solid waste incinerator in Korea

In Korea, the amount of greenhouse gases released due to waste materials was 14,800,000 t CO₂eq in 2012, which increased from 5,000,000 t CO₂eq in 2010. This included the amount released due to incineration, which has gradually increased since 2010. Incineration was found to be the biggest contributor to greenhouse gases, with 7,400,000 t CO₂eq released in 2012. Therefore, with regards to the trading of greenhouse gases emissions initiated in 2015 and the writing of the national inventory report, it is important to increase the reliability of the measurements related to the incineration of waste materials.

This research explored methods for estimating the biomass fraction at Korean MSW incinerator facilities and compared the biomass fractions obtained with the different biomass fraction estimation methods. The biomass fraction was estimated by the method using default values of fossil carbon fraction suggested by IPCC, the method using the solid waste composition, and the method using incinerator flue gas.

The highest biomass fractions in Korean municipal solid waste incinerator facilities were estimated by the IPCC Default method, followed by the MSW analysis method and the Flue gas analysis method. Therefore, the difference in the biomass fraction estimate was the greatest between the IPCC Default and the Flue gas analysis methods. The difference between the MSW analysis and the flue gas analysis methods was smaller than the difference with IPCC Default method. This suggested that the use of the IPCC default method cannot reflect the characteristics of Korean waste incinerator facilities and Korean MSW.

Implications: Incineration is one of most effective methods for disposal of municipal solid waste (MSW). This paper investigates the applicability of using biomass content to estimate the amount of CO₂ released, and compares the biomass contents determined by different methods in order to establish a method for estimating biomass in the MSW incinerator facilities of Korea. After analyzing the biomass contents of the collected solid waste samples and the flue gas samples, the results were compared with the Intergovernmental Panel on Climate Change (IPCC) method, and it seems that to calculate the biomass fraction it is better to use the flue gas analysis method than the IPCC method. It is valuable to design and operate a real new incineration power plant, especially for the estimation of greenhouse gas emissions.     

Freight shipment modal split and its environmental impacts: An exploratory study

Freight transportation activities are responsible for a large share of air pollution and greenhouse gas emissions in the United States. Various freight transportation modes have significantly different impacts on air quality and environmental sustainability, and this highlights the need for a better understanding of interregional freight shipment mode choices. This paper develops a binomial logit market share model to predict interregional freight modal share between truck and rail as a function of freight and shipment characteristics. This model can be used to estimate the impacts of various factors, such as oil price, on shippers’ mode choice decisions. A set of multiyear freight and geographical information databases was integrated to construct regression models for typical freight commodities. The atmospheric impact levels incurred by different freight modal choice decisions are analyzed to provide insights on the relationship among freight modal split, oil price change, and air quality.

Implications:

Freight transportation has become a major source of energy consumption and air pollution, and emissions rates vary significantly across different modes. Understanding freight shipment mode choice under various economic and engineering factors will help assess the environmental impacts of freight shipment systems at the national level. This paper develops a binomial logit model for two dominating modes (truck and rail) and shows how this model is incorporated into an environmental impact analysis. The framework will be useful to policy makers to assess the impacts of freight movements on air quality and public health and to mitigate those adverse impacts.     

Validation of a new method for measuring and continuously monitoring the efficiency of industrial flares

A new method has been developed for a direct and remote measurement of industrial flare combustion efficiency (CE). The method is based on a unique hyper-spectral or multi-spectral Infrared (IR) imager which provides a high frame rate, high spectral selectivity and high spatial resolution. The method can be deployed for short-term flare studies or for permanent installation providing real-time continuous flare CE monitoring.

In addition to the measurement of CE, the method also provides a measurement for level of smoke in the flare flame regardless of day or night. The measurements of both CE and smoke level provide the flare operator with a real-time tool to achieve “incipient smoke point” and optimize flare performance.

The feasibility of this method was first demonstrated in a bench scale test. The method was recently tested on full scale flares along with extractive sampling methods to validate the method. The full scale test included three types of flares – steam assisted, air assisted, and pressure assisted. Thirty-nine test runs were performed covering a CE range of approximately 60-100%. The results from the new method showed a strong agreement with the extractive methods (r²=0.9856 and average difference in CE measurement=0.5%).

Implications: Because industrial flares are operated in the open atmosphere, direct measurement of flare combustion efficiency (CE) has been a long-standing technological challenge. Currently flare operators do not have feedback in terms of flare CE and smoke level, and it is extremely difficult for them to optimize flare performance and reduce emissions. The new method reported in this paper could provide flare operators with real-time data for CE and smoke level so that flare operations can be optimized. In light of EPA’s focus on flare emissions and its new rules to reduce emissions from flares, this policy-relevant development in flare CE monitoring is brought to the attention of both the regulating and regulated communities.     

A novel bagging ensemble approach for predicting summertime ground-level ozone concentration

Ozone pollution appears as a major air quality issue, e.g. for the protection of human health and vegetation. Formation of ground level ozone is a complex photochemical phenomenon and involves numerous intricate factors most of which are interrelated with each other. Machine learning techniques can be adopted to predict the ground level ozone. The main objective of the present study is to develop the state-of-the-art ensemble bagging approach to model the summer time ground level ozone in an industrial area comprising a hazardous waste management facility. In this study, the feasibility of using ensemble model with seven meteorological parameters as input variables to predict the surface level O3 concentration. Multilayer perceptron, RTree, REPTree, and Random forest were employed as the base learners. The error measures used for checking the performance of each model includes IoAd, R2, and PEP. The model results were validated against an independent test data set. Bagged random forest predicted the ground level ozone better with higher Nash-Sutcliffe coefficient 0.93. This study scaffolded the current research gap in big data analysis identified with air pollutant prediction.

Implications: The main focus of this paper is to model the summer time ground level O₃ concentration in an Industrial area comprising of hazardous waste management facility. Comparison study was made between the base classifiers and the ensemble classifiers. Most of the conventional models can well predict the average concentrations. In this case the peak concentrations are of importance as it has serious effect on human health and environment. The models developed should also be homoscedastic.     

AERMOD performance evaluation for three coal-fired electrical generating units in Southwest Indiana

An evaluation of the steady-state dispersion model AERMOD was conducted to determine its accuracy at predicting hourly ground-level concentrations of sulfur dioxide (SO₂) by comparing model-predicted concentrations to a full year of monitored SO₂ data. The two study sites are comprised of three coal-fired electrical generating units (EGUs) located in southwest Indiana. The sites are characterized by tall, buoyant stacks, flat terrain, multiple SO₂ monitors, and relatively isolated locations. AERMOD v12060 and AERMOD v12345 with BETA options were evaluated at each study site. For the six monitor–receptor pairs evaluated, AERMOD showed generally good agreement with monitor values for the hourly 99th percentile SO₂ design value, with design value ratios that ranged from 0.92 to 1.99. AERMOD was within acceptable performance limits for the Robust Highest Concentration (RHC) statistic (RHC ratios ranged from 0.54 to 1.71) at all six monitors. Analysis of the top 5% of hourly concentrations at the six monitor–receptor sites, paired in time and space, indicated poor model performance in the upper concentration range. The amount of hourly model predicted data that was within a factor of 2 of observations at these higher concentrations ranged from 14 to 43% over the six sites. Analysis of subsets of data showed consistent overprediction during low wind speed and unstable meteorological conditions, and underprediction during stable, low wind conditions. Hourly paired comparisons represent a stringent measure of model performance; however, given the potential for application of hourly model predictions to the SO₂ NAAQS design value, this may be appropriate. At these two sites, AERMOD v12345 BETA options do not improve model performance.

Implications:

A regulatory evaluation of AERMOD utilizing quantile-quantile (Q–Q) plots, the RHC statistic, and 99th percentile design value concentrations indicates that model performance is acceptable according to widely accepted regulatory performance limits. However, a scientific evaluation examining hourly paired monitor and model values at concentrations of interest indicates overprediction and underprediction bias that is outside of acceptable model performance measures. Overprediction of 1-hr SO₂ concentrations by AERMOD presents major ramifications for state and local permitting authorities when establishing emission limits.     

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.     

Long-term dust aerosol production from natural sources in Iceland

Iceland is a volcanic island in the North Atlantic Ocean with maritime climate. In spite of moist climate, large areas are with limited vegetation cover where >40% of Iceland is classified with considerable to very severe erosion and 21% of Iceland is volcanic sandy deserts. Not only do natural emissions from these sources influenced by strong winds affect regional air quality in Iceland (“Reykjavik haze”), but dust particles are transported over the Atlantic ocean and Arctic Ocean >1000 km at times. The aim of this paper is to place Icelandic dust production area into international perspective, present long-term frequency of dust storm events in northeast Iceland, and estimate dust aerosol concentrations during reported dust events.

Meteorological observations with dust presence codes and related visibility were used to identify the frequency and the long-term changes in dust production in northeast Iceland. There were annually 16.4 days on average with reported dust observations on weather stations within the northeastern erosion area, indicating extreme dust plume activity and erosion within the northeastern deserts, even though the area is covered with snow during the major part of winter. During the 2000s the highest occurrence of dust events in six decades was reported. We have measured saltation and Aeolian transport during dust/volcanic ash storms in Iceland, which give some of the most intense wind erosion events ever measured.

Icelandic dust affects the ecosystems over much of Iceland and causes regional haze. It is likely to affect the ecosystems of the oceans around Iceland, and it brings dust that lowers the albedo of the Icelandic glaciers, increasing melt-off due to global warming. The study indicates that Icelandic dust may contribute to the Arctic air pollution.

Implications: Long-term records of meteorological dust observations from Northeast Iceland indicate the frequency of dust events from Icelandic deserts. The research involves a 60-year period and provides a unique perspective of the dust aerosol production from natural sources in the sub-Arctic Iceland. The amounts are staggering, and with this paper, it is clear that Icelandic dust sources need to be considered among major global dust sources. This paper presents the dust events directly affecting the air quality in the Arctic region.     

Development of operating mode distributions for different types of roadways under different congestion levels for vehicle emission assessment using MOVES

The Motor Vehicle Emission Simulator (MOVES) quantifies emissions as a function of vehicle modal activities. Hence, the vehicle operating mode distribution is the most vital input for running MOVES at the project level. The preparation of operating mode distributions requires significant efforts with respect to data collection and processing. This study is to develop operating mode distributions for both freeway and arterial facilities under different traffic conditions. For this purpose, in this study, we (1) collected/processed geographic information system (GIS) data, (2) developed a model of CO₂ emissions and congestion from observations, (3) implemented the model to evaluate potential emission changes from a hypothetical roadway accident scenario. This study presents a framework by which practitioners can assess emission levels in the development of different strategies for traffic management and congestion mitigation.

Implications: This paper prepared the primary input, that is, the operating mode ID distribution, required for running MOVES and developed models for estimating emissions for different types of roadways under different congestion levels. The results of this study will provide transportation planners or environmental analysts with the methods for qualitatively assessing the air quality impacts of different transportation operation and demand management strategies.     

Evaluation of gas well setback policy in the Marcellus Shale region of Pennsylvania in relation to emissions of fine particulate matter

Shale gas has become an important strategic energy source with considerable potential economic benefits and the potential to reduce greenhouse gas emissions in so far as it displaces coal use. However, there still exist environmental health risks caused by emissions from exploration and production activities. In the United States, states and localities have set different minimum setback policies to reduce the health risks corresponding to the emissions from these locations, but it is unclear whether these policies are sufficient. This study uses a Gaussian plume model to evaluate the probability of exposure exceedance from EPA concentration limits for PM2.5 at various locations around a generic wellsite in the Marcellus shale region. A set of meteorological data monitored at ten different stations across Marcellus shale gas region in Pennsylvania during 2015 serves as an input to this model. Results indicate that even though the current setback distance policy in Pennsylvania (500 ft. or 152.4 m) might be effective in some cases, exposure limit exceedance occurs frequently at this distance with higher than average emission rates and/or greater number of wells per wellpad. Setback distances should be 736 m to ensure compliance with the daily average concentration of PM2.5, and a function of the number of wells to comply with the annual average PM2.5 exposure standard.

Implications: The Marcellus Shale gas is known as a significant source of criteria pollutants and studies show that the current setback distance in Pennsylvania is not adequate to protect the residents from exceeding the established limits. Even an effective setback distance to meet the annual exposure limit may not be adequate to meet the daily limit. The probability of exceeding the annual limit increases with number of wells per site. We use a probabilistic dispersion model to introduce a technical basis to select appropriate setback distances.     

Multiobjective optimization model of intersection signal timing considering emissions based on field data: A case study of Beijing

Most existing signal timing models are aimed to minimize the total delay and stops at intersections, without considering environmental factors. This paper analyzes the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. First, considering the different operating modes of cruising, acceleration, deceleration, and idling, field data of emissions and Global Positioning System (GPS) are collected to estimate emission rates for heavy-duty and light-duty vehicles. Second, multiobjective signal timing optimization model is established based on a genetic algorithm to minimize delay, stops, and emissions. Finally, a case study is conducted in Beijing. Nine scenarios are designed considering different weights of emission and traffic efficiency. The results compared with those using Highway Capacity Manual (HCM) 2010 show that signal timing optimized by the model proposed in this paper can decrease vehicles delay and emissions more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.

Implications: Vehicle emissions are heavily at signal intersections in urban area. The multiobjective signal timing optimization model is proposed considering the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. The results indicate that signal timing optimized by the model proposed in this paper can decrease vehicle emissions and delays more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.     

Modeling of methane oxidation in landfill cover soil using an artificial neural network

Knowing the fraction of methane (CH₄) oxidized in landfill cover soils is an important step in estimating the total CH₄ emissions from any landfill. Predicting CH₄ oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH₄ oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O₂) concentration at a depth of 10 cm in cover soil, and CH₄ concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH₄ oxidation with a MSE of 0.0082, a coefficient of determination (R ²) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.

Implications:

To date, no attempts have been made to predict the percent of CH₄ oxidation within landfill cover soils using an ANN. This paper presents modeling of CH₄ oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH₄ oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH₄ oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH₄ emission and how much it contributes to global warming.     

The air quality forecast rote: Recent changes and future challenges

Air quality forecasting is a recent development, with most programs initiated only in the last 20 years. During the last decade, forecast preparation procedure—the forecast rote—has changed dramatically. This paper summarizes the unique challenges posed by air quality forecasting, details the current forecast rote, and analyzes prospects for future improvements. Because air quality forecasts must diagnose and predict several pollutants and their precursors in addition to standard meteorological variables, it is, compared with weather forecasts, a higher-uncertainty forecast. Forecasters seek to contain the uncertainty by “anchoring” the forecast, using an a priori field, and then “adjusting” the forecast using additional information. The air quality a priori, or first guess, field is a blend of past, current, and near-term future observations of the pollutants of interest, on both local and regional scales, and is typically coupled with predicted air parcel trajectories. Until recently, statistical methods, based on long-term training data sets, were used to adjust the first guess. However, reductions in precursor emissions in the United States, beginning in the late 1990s and continuing to the present, eroded the stationarity assumption for the training data sets and degraded forecast skill. Beginning in the mid-2000s, output from modified numerical air quality prediction (NAQP) models, originally developed to test pollution control strategies, became available in near real time for forecast support. The current adjustment process begins with the analyses and postprocessing of individual NAQP models and their ad hoc ensembles, often in concert with new statistical techniques. The final adjustment step uses forecaster expertise to assess the impact of mesoscale features not resolved by the NAQP models. It is expected that advances in model resolution, chemical data assimilation, and the formulation of emissions fields will improve mesoscale predictions by NAQP models and drive future changes in the forecast rote.

Implications: Routine air quality forecasts are now issued for nearly all the major U.S. metropolitan areas. Methods of forecast preparation—the forecast rote—have changed significantly in the last decade. Numerical air quality models have matured and are now an indispensable part of the forecasting process. All forecasting methods, particularly statistically based models, must be continually calibrated to account for ongoing local- and regional-scale emission reductions.     

Research and proposal on selective catalytic reduction reactor optimization for industrial boiler

The advanced computational fluid dynamics (CFD) software STAR-CCM+ was used to simulate a denitrification (De-NOx) project for a boiler in this paper, and the simulation result was verified based on a physical model. Two selective catalytic reduction (SCR) reactors were developed: reactor 1 was optimized and reactor 2 was developed based on reactor 1. Various indicators, including gas flow field, ammonia concentration distribution, temperature distribution, gas incident angle, and system pressure drop were analyzed. The analysis indicated that reactor 2 was of outstanding performance and could simplify developing greatly. Ammonia injection grid (AIG), the core component of the reactor, was studied; three AIGs were developed and their performances were compared and analyzed. The result indicated that AIG 3 was of the best performance. The technical indicators were proposed for SCR reactor based on the study.

Implications: Flow filed distribution, gas incident angle, and temperature distribution are subjected to SCR reactor shape to a great extent, and reactor 2 proposed in this paper was of outstanding performance; ammonia concentration distribution is subjected to ammonia injection grid (AIG) shape, and AIG 3 could meet the technical indicator of ammonia concentration without mounting ammonia mixer. The developments above on the reactor and the AIG are both of great application value and social efficiency.