基于SIMULINK的硝化反应动力学模型的仿真

利用MATLAB/SIMULINK对序批式生物膜反应器内的氨氧化细菌与亚硝酸盐氧化菌的生化反应进行仿真预测。模型的验证结果表明,适当的选择模型中的溶解氧浓度、碱度以及温度3种参数,SIMULINK仿真动力学模型能够比较准确地对氨氧化细菌与亚硝酸氧化细菌处理生活污水的过程进行仿真和预测.NH4+-N、NO2--N和NO3--N 3种基质仿真值的绝对平均误差最大为15.88,最小为1.13;NH4+-N、NO2--N和NO3--N的Nash.Suttcliffe模拟效率系数分别为99.36%、98.64%和99.25%;此外,还对SIMULINK仿真动力学模型中的溶解氧浓度、碱度以及温度进行了灵敏度分析,结果表明,温度的灵敏度最大、溶解氧次之、碱度灵敏度相对最小。     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

     

Organic pollution of the Büyük Menderes River,Turkey and effects on aquaculture

Water quality was measured at eight stations on the Buyuk Menderes River in Turkey (Ad?güzel dam, Yenice regülator, Sarayköy bridge, Feslek regülator, Yenipazar bridge, Ayd?n bridge, Koçarl? bridge, Söke regülator) between 2000 and 2013 in February, April, June, August, October and December. The resulting data were evaluated in terms of biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), ammoniac–nitrogen (NH₃–N), nitrite–nitrogen (NO₂–N), nitrate–nitrogen (NO₃–N) and orthophosphate (o-PO₄) aquaculture. According to the analysis, while river water pollution generally varied during each year, samples from certain measurement points demonstrated high pollution levels throughout the year. In this study, water parameters were classified according to the “Turkish water pollution control regulation.” The studied parameters are also compared with TS 266 and WHO guidelines. While levels at Sarayköy station were generally higher than other stations, values at Ad?güzel dam were the lowest, giving it the best water quality of the eight stations. The highest values on a yearly basis were obtained in 2007 due to the severe drought in the Menderes basin within which irrigation water levels fell to 4255 m³/ha. The BOD, COD levels are the lowest in 2009 and highest in 2007; the DO level is lowest in 2007 and highest in 2009; NH₃–N, NO₂–N, and NO₃–N parameters are the lowest in 2007 and highest in 2009; and the o-PO₄ are at the lowest level in 2004 and seen as the highest in 2007. Analysis of the data was performed by SPSS 21 statistics program. One direction ANOVA was applied to the data, which were also subject to Tukey multiple comparison tests. Differences between groups were evaluated at p?<?0.05. Box–plot graphs were used to demonstrate the data distribution. In the study, it was analyzed, the effect of fish species and pollution involved in the Büyük Menderes River so far on fish species.     

用微电极测定曝气量对SBR系统中硝化作用的影响

为了研究曝气量对硝化作用的影响,实验采用3个相同的SBR装置,分别在曝气量为4、10和16 L/h的条件下处理人工污水,并采用自制的溶解氧、NO3-、NH4+和pH微电极测定了活性污泥絮体内部微元环境中相应基质的浓度。结果表明,曝气量为4 L/h时,活性污泥絮体内存在厌氧微区,NO3--N浓度减小了,发生了反硝化作用;而曝气量为10 L/h和16 L/h时,活性污泥絮体内发生的都是硝化反应,且NH4+-N浓度的减小量、NO3--N浓度的增大量都随着曝气量的增大而增大,pH随着曝气量的增大而减小。     

CAS工艺与AOE工艺曝气池水质信息场的测量与分析

为了研究曝气池空间内水质的分布及变化规律,采用了YSI6600 V2多参数水质检测仪对成都市三瓦窑污水处理厂的一期工程传统污泥法(CAS)、二期工程缺氧、好氧和内源呼吸(AOE)工艺中的曝气池分别进行了包括DO、NH3-N、NO3--N、pH、氯化物和温度等水质指标的场的检测,通过分析这些数据,揭示了曝气池内部水质变化的规律,并比较了CAS工艺与AOE工艺内水质处理效果的差别。结果表明,(1)溶解氧沿池深方向呈现出一定的分布规律,CAS工艺中随池深增加,溶解氧增加,AOE工艺中随着池深的增加,溶解氧降低;(2)AOE工艺对氨氮在O区的去除率可达97.64%,明显高于CAS工艺的41.76%;(3)沿曝气池长度测量溶解氧空间分布规律,参照理论曲线及依据水质处理要求,调整曝气量的方法具有长期运行经济、水质处理效果好的特点。     

Sensitivity analyses of factors influencing CMAQ performance for fine particulate nitrate

Improvement of air quality models is required so that they can be utilized to design effective control strategies for fine particulate matter (PM_2.5). The Community Multiscale Air Quality modeling system was applied to the Greater Tokyo Area of Japan in winter 2010 and summer 2011. The model results were compared with observed concentrations of PM_2.5 sulfate (SO₄^2-), nitrate (NO₃^?) and ammonium, and gaseous nitric acid (HNO₃) and ammonia (NH₃). The model approximately reproduced PM_2.5 SO₄^2? concentration, but clearly overestimated PM_2.5 NO₃^? concentration, which was attributed to overestimation of production of ammonium nitrate (NH₄NO₃). This study conducted sensitivity analyses of factors associated with the model performance for PM_2.5 NO₃^? concentration, including temperature and relative humidity, emission of nitrogen oxides, seasonal variation of NH₃ emission, HNO₃ and NH₃ dry deposition velocities, and heterogeneous reaction probability of dinitrogen pentoxide. Change in NH₃ emission directly affected NH₃ concentration, and substantially affected NH₄NO₃ concentration. Higher dry deposition velocities of HNO₃ and NH₃ led to substantial reductions of concentrations of the gaseous species and NH₄NO₃. Because uncertainties in NH₃ emission and dry deposition processes are probably large, these processes may be key factors for improvement of the model performance for PM_2.5 NO₃^?.

Implications: The Community Multiscale Air Quality modeling system clearly overestimated the concentration of fine particulate nitrate in the Greater Tokyo Area of Japan, which was attributed to overestimation of production of ammonium nitrate. Sensitivity analyses were conducted for factors associated with the model performance for nitrate. Ammonia emission and dry deposition of nitric acid and ammonia may be key factors for improvement of the model performance.     

The Use of Ambient Measurements To Identify which Precursor Species Limit Aerosol Nitrate Formation

ABSTRACT

A thermodynamic equilibrium model was used to investigate the response of aerosol NO₃ to changes in concentrations of HNO₃, NH₃, and H₂SO₄. Over a range of temperatures and relative humidities (RHs), two parameters provided sufficient information for indicating the qualitative response of aerosol NO₃. The first was the excess of aerosol NH₄ ⁺ plus gas-phase NH₃ over the sum of HNO₃, particulate NO₃, and particulate SO₄ ^2- concentrations. The second was the ratio of particulate to total NO₃ concentrations. Computation of these quantities from ambient measurements provides a means to rapidly analyze large numbers of samples and identify cases in which inorganic aerosol NO₃ formation is limited by the availability of NH₃. Example calculations are presented using data from three field studies. The predictions of the indicator variables and the equilibrium model are compared.     

Ozone levels in the empty quarter of Saudi Arabia—application of adaptive neuro-fuzzy model

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott’s index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.     

垃圾渗滤液处理工艺运行参数优化与技术比较

垃圾渗滤液是公认的一种成分复杂且难以处理的高浓度有机废水,笔者在北京市海淀区六里屯垃圾填埋场通过2007—2010年垃圾渗滤液处理的工程实践,以COD和氨氮的去除率为指标,研究了不同的垃圾渗滤液处理工艺组合,以及不同运行参数条件下对垃圾渗滤液的处理效果。结果表明,在中温UASB和A/O的平均水力停留时间(HRT)缩短1/3的情况下,通过改进A/O段曝气方式,优化系统的pH、DO等运行参数,用MBR替代絮凝工艺,使整个组合工艺对COD的年平均去除率达到了94.3%,氨氮的去除率维持在99.5%以上,出水氨氮稳定在10 mg/L以内,而改造前的COD与氨氮的年平均去除率仅为82.2%与55.3%。与改造前的UASB+A/O+絮凝工艺组合相比,改造后的UASB+A/O+MBR工艺组合具有更高的污染物去除能力、更好的抗缓冲性和稳定性。     

Evaluating Environmental Parameters for Minimum Ammonium Losses during Composting of Trimming Residues

Abstract

A neural fuzzy system was used to investigate the influence of environmental variables (time, aeration, moisture, and particle size) on composting parameters (pH, organic matter [OM], nitrogen [N], ammonium nitrogen [NH₄ ⁺-N] and nitrate nitrogen [NO₃ ^--N]). This was to determine the best composting conditions to ensure the maximum quality on the composts obtained with the minimum ammonium losses. A central composite experimental design was used to obtain the neural fuzzy model for each dependent variable. These models, consisting of the four independent process variables, were found to accurately describe the composting process (the differences between the experimental values and those estimated by using the equations never exceeded 5–10% of the former). Results of the modeling showed that creating a product with acceptable chemical properties (pH, NH₄ ⁺-N and NO₃ ^--N) entails operating at medium moisture content (55%) and medium to high particle size (3–5 cm). Moderate to low aeration (0.2 L air/min · kg) would be the best compromise to compost this residue because of the scant statistical influence of this independent variable.     

The Statistical Form for the Ambient Participate Standard Annual Arithmetic Mean vs Annual Geometric Mean

Abstract

The long-term stability of a biofilter loaded with waste gases containing NH₃ concentrations larger than 100 ppmv was studied in a laboratory-scale compost reactor. At an empty bed residence time (τ) of 21 sec, elimination capacities of more than 300 g NH₃/m³/day were obtained at elimination efficiencies up to 87%. Because of absorption and nitrification, almost 80% of the NH₃-N eliminated from the waste gas could be recovered in the compost as NH₄+-N or NO₂ ^?/NO₃ ^?-N. The high elimination capacities could be maintained as long as the NH₄+/NO_x concentration in the carrier material was less than 4 g NH₄+/NO_x ^?-N/kg wet compost. Above this critical value, osmotic effects inhibited the nitrifying activity, and the elimination capacity for NH₃ decreased. To restore the biofilter performance, a carbon source (methanol) was added to reduce NH₄+/NO_x ^? accumulated in the compost. Results indicate that methylotrophic microorganisms did convert NH₄+/NO_x ^? into biomass, as long as the NO₃ ^? content in the compost was larger than 0.1 g NO₃ ^?-N/kg compost. Removal efficiencies of CH₃OH of more than 90% were obtained at volumetric loads up to 11,000 g CH₃OH/m³/day. It is shown that addition of CH₃OH is a suitable technique for regenerating the compost material from osmotic inhibition as a result of high NH₃ loading. The biofilter was operated for 4 months with alternating loading of NH₃ and CH₃OH.     

Acidification of a solonetzic soil by nitrogenous fertilizers

Abstract

Annual applications of (NH₄)₂SO₄ NH₄NO₃ and urea on a Solonetzic soil at 112 kg N/ha for 10 consecutive years reduced pH levels from 5.6 for the check to 4.4, 4.9 and 5.3, respectively for (NH₄)₂SO₄, NH₄NO₃ and urea. (NH₄)₂SO₄ generated twice as much exchange acidity as NH₄NO₃ and four times as much as urea. Net extractable cations leached from the Ap horizon closely approximated the amount of exchange acidity generated by (NH₄)₂SO₄ and NH₄NO₃ fertilizers. The levels of soil extractable Al and Mn were greatly enhanced by (NH₄)₂SO₄ as were plant contents. Similar acidifying effects to that produced by the (NH₄)₂SO₄ occurred when NH₄NO₃ was applied at 300 kg N/ha annually for 12 consecutive years in another field experiment on the same soil. Liming samples of the field (NH₄)₂SO₄ acidified soils in the greenhouse, significantly increased yields and lowered the Al and Mn contents of the plants to normal levels.     

生物脱氮与甲烷化耦合的EGSB-BAF工艺模拟

以活性污泥3号模型(ASM3)为平台,通过引入厌氧氨氧化和甲烷化过程、以2步硝化-反硝化取代1步硝化-反硝化过程以及区分硝酸盐和亚硝酸盐条件下的内源呼吸过程,建立了同时具有甲烷化、厌氧氨氧化和2步硝化反硝化功能的EGSB-BAF工艺模型.该模型包括5种微生物、28个生物过程,同时考虑了温度、pH值和抑制性物质对生物过程的影响.以实验室EGSB-BAF集成工艺的实验数据,结合灵敏度分析对部分模型参数进行了校核.采用校核后的模型对集成工艺EGSB段和BAF段出水的COD、NH4+-N、NO2--N和NO3--N浓度进行模拟,结果表明,EGSB段和BAF段的出水中COD、NH4+-N、NO2--N和NO3--N浓度的模拟值与实测值的误差在可接受范围内.表明模型能够描述工艺的主要生物反应过程,可以作为指导工艺研究、设计和运行优化的手段.     

分段进水SBR短程生物脱氮过程中N2O产生及控制

利用SBR,控制曝气量为60 L/h,利用在线pH曲线控制曝气时间,成功实现了短程生物脱氮过程,并考察了不同进水方式下SBR运行性能及N2O产量。结果表明,分段进水能够有效降低短程生物脱氮过程中外加碳源投加量。在原水进水碳氮比较低时,采用递增进水量的进水方式,能够有效降低生物脱氮过程中NO-2积累量,从而降低系统N2O产量。1次进水、2次等量进水和2次递增进水方式下,生物脱氮过程中N2O产量分别为11.1、8.86和5.04 mg/L。硝化过程中NO-2-N的积累是导致系统N2O产生的主要原因。部分氨氧化菌(AOB)在限氧条件下以NH+4-N作为电子供体,NO-2-N作为电子受体进行反硝化,最终产物是N2O。     

Cr(VI) and COD removal from landfill leachate by polyculture constructed wetland at a pilot scale

Four subsurface horizontal-flow constructed wetlands (CWs) at a pilot scale planted with a polyculture of the tropical plants Gynerium sagittatum (Gs), Colocasia esculenta (Ce) and Heliconia psittacorum (He) were evaluated for 7 months. The CW cells with an area of 17.94 m² and 0.60 m (h) each and 0.5 m of gravel were operated at continuous gravity flow (Q?=?0.5 m³ day^?1) and a theoretical HRT of 7 days each and treating landfill leachate for the removal of filtered chemical oxygen demand (COD_f), BOD₅, TKN, NH₄ ⁺, NO₃ ^?, PO₄ ^3?–P and Cr(VI). Three CWs were divided into three sections, and each section (5.98 m²) was seeded with 36 cuttings of each species (plant density of six cuttings per square metre). The other unit was planted randomly. The final distributions of plants in the bioreactors were as follows: CW I (He-Ce-Gs), CW II (randomly), CW III (Ce-Gs-He) and CW IV (Gs-He-Ce). The units received effluent from a high-rate anaerobic pond (BLAAT®). The results show a slightly alkaline and anoxic environment in the solid-liquid matrix (pH?=?8.0; 0.5–2 mg L^?1 dissolved oxygen (DO)). COD_f removal was 67 %, BOD₅ 80 %, and TKN and NH₄ ⁺ 50–57 %; NO₃ ^? effluents were slightly higher than the influent, PO₄ ^3?–P (38 %) and Cr(VI) between 50 and 58 %. CW IV gave the best performance, indicating that plant distribution may affect the removal capacity of the bioreactors. He and Gs were the plants exhibiting a translocation factor (TF) of Cr(VI) >1. The evaluated plants demonstrated their suitability for phytoremediation of landfill leachate, and all of them can be categorized as Cr(VI) accumulators. The CWs also showed that they could be a low-cost operation as a secondary system for treatment of intermediated landfill leachate (LL).     

Redundancy analysis for characterizing the groundwater quality in coastal industrial areas

Groundwater quality in coastal area has been an issue of interest because of excessive groundwater extraction for human use, for example, industrialization, irrigation, which can lead to saltwater intrusion. The study develops an integrated data analysis procedure based on multivariate statistics principal component analysis (PCA), hierarchical cluster analysis (HCA) and redundancy analysis (RDA), to determine the effects of key environmental conditions on the formulation of groundwater pollutants. This proposed method was demonstrated by analyzing groundwater quality monitoring data collected between 2011 and 2014 from four coastal industrial areas in Changhua county of Taiwan, namely Chuansing, Xianxi, Lukang and Fangyuan industrial parks. First, different environmental conditions in each industrial region were explored by PCA. The spatial hierarchy and spatial distribution of pollutant categories were then identified using HCA with the kriging method. Finally, the effect of environmental conditions on constitutive pollutants were identified with RDA. The three environmental patterns identified from the analytical results in Chuansing, Lukang and Xianxi were the salination factor (including conductivity and general hardness (GH)), water level and redox condition (including dissolved oxygen and oxidation–reduction potential). Fangyuan industrial park had only two patterns, namely salination (including conductivity and GH) and oxygen content (including DO and depth). The pollutant category indicated high concentrations of all pollutants in Chuansing and Fangyuan, and higher concentration of SO₄^2?, TDS, Cl^? in Xianxi, and of NH₃-N, Mn, Fe and TOC in Lukang. According to RDA results, salination caused the high concentrations of NH₃N, Cl^?, TDS in Chuansing, and of Cl^?, TDS and SO₄^2? in Xianxi and Lukang. Additionally, salination caused high concentrations of Fe in both Lukang and Fangyuan industrial parks in combination with those three pollutants. The redox condition was linked to high content of NO₃^? in Chuansing and Lukang, and of TOC in Xianxi. In Fangyuan industrial park, NO₃^? was also linked to high oxygen concentration. In summary, the combination of PCA, HCA and RDA enables the analysis of monitoring data to support policy decision-making.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Characterization of atmospheric air pollutants at two sites in northern Kyushu,Japan – chemical form,and chemical reaction

Airborne gaseous and particulate matter in winter was measured over for 37 days in January and December 1997 at 2 sampling sites in northern Kyushu, Japan. One sampling site, Goto Island (an isolated island in the East China Sea), was about 200 km southwest of the other sampling site, Dazaifu city. In winter, acidic sulfates generated over the East Asian continent were transported to northwest Kyushu, to places such as Goto Island and the inland Kyushu area, and high sulfate concentrations were observed at the 2 sampling sites when strong NW winds blew. Acidity around Goto was mainly influenced by particulate NH₄HSO₄. The concentrations of NH₃ at Goto Island were lower than at Dazaifu city. The difference in NH₃ levels at the 2 sampling sites plays an important role in the chemical forms and sizes of the particulate matter. Nitrates at Goto Island were mostly present as NaNO₃ and Ca(NO₃)₂ in coarse-size particles. During the process of long-range transport of air pollutants from the Asian continent to Goto, gaseous HNO₃ was produced by a photochemical reactions of nitrogen oxides in the atmosphere, and particulate NaNO₃ and gaseous HCl were formed by a chlorine-loss reaction between NaCl and gaseous HNO₃. When strong NW winds blew, acidic sulfates together with some of the NaNO₃ and/or Ca(NO₃)₂ and some of gaseous HCl and HNO₃, which exist in the sea to the west of Kyushu and Goto Island, were transported to inland Kyushu such as Dazaifu city. During the process of transport, most of the acidic sulfates and acidic gases were mixed with regional air pollutants such as chlorides and nitrates existing around Dazaifu city, and neutralized forming (NH₄)₂SO₄, NH₄Cl and NH₄NO₃ in an environment of excess NH₃. Therefore, the main chemical forms of NO₃⁻ at Dazaifu city varied day-by-day from fine-sized NH₄NO₃ to coarse-sized NaNO₃ and/or Ca(NO₃)₂. The appearance of NO₃⁻ in coarse-size particles at Dazaifu city was due to the transport of NO₃⁻ from around the sea to the west of Kyushu.     

Modeling the long-term frequency distribution of regional ozone concentrations

Efficient methods are developed for modeling emissions – air quality relationships that govern ozone and NO₂ concentrations over very long periods of time. A baseline model evaluation study is conducted to assess the accuracy and speed with which the relationship between pollutant emissions and the frequency distribution of O₃ concentrations throughout the year can be computed along with annual average NO₂ values using a deterministic photochemical airshed model driven by automated objective analysis of measured meteorological parameters. Methods developed are illustrated by application to the air quality situation that exists in Southern California. Model performance statistics for O₃ are similar to the results obtained in previous short-term episodic model evaluation studies that were based on hand-crafted meteorological inputs that are supplemented by expensive field measurement campaigns. Model predictions at one of the highest NO₂ concentration sites in the US indicate that measured violation of the US annual average NO₂ air quality standard at that site occurs because other species such as HNO₃ and PAN are measured as if they were NO₂ by the chemiluminescent NO_x monitors in current use.     

Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II—particulate matter

This paper is Part II in a pair of papers that examines the results of the Community Multiscale Air Quality (CMAQ) model version 4.5 (v4.5) and discusses the potential explanations for the model performance characteristics seen. The focus of this paper is on fine particulate matter (PM_2.5) and its chemical composition. Improvements made to the dry deposition velocity and cloud treatment in CMAQ v4.5 addressing compensating errors in 36-km simulations improved particulate sulfate (SO₄²⁻) predictions. Large overpredictions of particulate nitrate (NO₃⁻) and ammonium (NH₄⁺) in the fall are likely due to a gross overestimation of seasonal ammonia (NH₃) emissions. Carbonaceous aerosol concentrations are substantially underpredicted during the late spring and summer months, most likely due, in part, to a lack of some secondary organic aerosol (SOA) formation pathways in the model. Comparisons of CMAQ PM_2.5 predictions with observed PM_2.5 mass show mixed seasonal performance. Spring and summer show the best overall performance, while performance in the winter and fall is relatively poor, with significant overpredictions of total PM_2.5 mass in those seasons. The model biases in PM_2.5 mass cannot be explained by summing the model biases for the major inorganic ions plus carbon. Errors in the prediction of other unspeciated PM_2.5 (PM_Other) are largely to blame for the errors in total PM_2.5 mass predictions, and efforts are underway to identify the cause of these errors.