Pollution source identification using a coupled diffusion model with a genetic algorithm

A new approach for the source quantification has been developed on the basis of real air pollutant hourly concentrations of SO₂, measured by three monitoring stations, during 9 h, around a group of three industrial sources. This inverse problem has been solved by coupling a direct model of diffusion (Pasquill’s Gaussian model) with a genetic algorithm, to search solutions leading to a minimum error between model outputs and measurements. The inversion performance depends on the relationship between the wind field and the configuration sources–receptors: good results are obtained when the monitoring stations are downwind from the sources, and in these cases, the order of magnitude of emissions is retrieved, sometimes with less than 10% error for at least two sources; there are some configurations (wind direction versus source and receptor locations) which do not permit to restore emissions. The latter situations reveal the need to conceive a specific network of sensors, taking into account the source locations and the most frequent weather patterns.     

Meteorological effects on sulphur dioxide concentrations on the area of Turbigo

This paper presents the relationship between meteorological parameters and sulphur dioxide concentrations in the area of Turbigo. A large number of sulphur dioxide sources are located in this area: they are both urban and industrial emissions. Mean half hourly SO₂ measurements were recorded over a three year period at five monitoring stations. Meteorological data were taken by a meteorological station in this network and included mean halfhourly measurements of wind direction and speed, air temperature, atmospheric pressure, relative humidity and rainfall. The SO₂ data were classified by the meteorological parameters, singly and in combination. From the analysis of the effects of various meteorological parameters, wind direction was found to be the parameter best correlated with pollution concentration. Additional results regarding the seasonal cycles of pollution levels are also presented. The work is also based on a set of statistical and graphic techniques.     

OPS: An operational atmospheric transport model for priority substances

The OPS-model (Operational model for Priority Substances) is a flexible atmospheric transport model for the calculation of concentration and deposition of low-reactive pollutants. The averaging period can be chosen from one month up to a period of more than 10 years. The receptor points may be defined on a regular grid in a model domain ranging from the local scale (several 100 m around a source) up to the scale of the European continent (ca. 2000×2000 km) or they may be defined by exact geographical (x,y) coordinates. The latter is for example applicable when the user wishes to compare the model results with measured values from monitoring stations. The emissions can be defined as any combination of point sources and (diffuse) area sources with variable horizontal dimensions. The model uses statistical meteorological data. The minimum set of required meteorological information consists of 6-hourly data for wind speed and direction, global radiation, temperature, and precipitation amount and duration. These data are pre-processed in a separate programme to calculate the necessary statistics.     

Space–time modelling of air quality for environmental-risk maps: A case study in South Portugal

Since the 1960s, there has been a strong industrial development in the Sines area, on the southern Atlantic coast of Portugal, including the construction of an important industrial harbour and of, mainly, petrochemical and energy-related industries. These industries are, nowadays, responsible for substantial emissions of SO₂, NO_x, particles, VOCs and part of the ozone polluting the atmosphere. The major industries are spatially concentrated in a restricted area, very close to populated areas and natural resources such as those protected by the European Natura 2000 network. Air quality parameters are measured at the emissions’ sources and at a few monitoring stations. Although air quality parameters are measured on an hourly basis, the lack of representativeness in space of these non-homogeneous phenomena makes even their representativeness in time questionable. Hence, in this study, the regional spatial dispersion of contaminants is also evaluated, using diffusive-sampler (Radiello Passive Sampler) campaigns during given periods. Diffusive samplers cover the entire space extensively, but just for a limited period of time.In the first step of this study, a space–time model of pollutants was built, based on a stochastic simulation—direct sequential simulation—with local spatial trend. The spatial dispersion of the contaminants for a given period of time—corresponding to the exposure time of the diffusive samplers—was computed by ordinary kriging. Direct sequential simulation was applied to produce equiprobable spatial maps for each day of that period, using the kriged map as a spatial trend and the daily measurements of pollutants from the monitoring stations as hard data.In the second step, the following environmental risk and costs maps were computed from the set of simulated realizations of pollutants: (i) maps of the contribution of each emission to the pollutant concentration at any spatial location; (ii) costs of badly located monitoring stations.     

An assessment of air-quality monitoring station locations based on satellite observations

ABSTRACT

Optimization of the locations of air quality monitoring stations has great importance in providing high-quality data for regional air pollution monitoring. To assess the representativeness of the locations of the current air quality monitoring stations, we propose a new method based on satellite observations by applying the stratified sampling approach. Unlike the traditional method, which relies on the simulated spatial distribution of air pollutants from dispersion models, we obtained the sampling population through observations from remote sensing. As a first step, the spatial distribution of aggregated air quality was obtained based on ground concentrations of particulate matter (aerodynamic diameters of less than 10 μm, PM₁₀), fine particulate matter (aerodynamic diameters of less than 2.5 μm, PM_2.5), nitrogen dioxide (NO₂), and sulphur dioxide (SO₂) derived from satellite observations. Second, the representativeness of locations of air quality monitoring stations was assessed using the stratified sampling method. The results demonstrated that air quality monitoring stations in Beijing-Tianjin-Hebei were clustered in areas with heavily polluted air, whereas the number of air quality monitoring stations was insufficient in areas with higher air quality. After optimization, the minimum relative error was only 6.77%. It is indicated that combing remote-sensing data with the stratified sampling approach has great potential in assessing the spatial representativeness of air quality monitoring stations.     

基于注意力机制和高斯概率估计的PM2.5浓度预测

作为衡量空气污染物浓度的重要指标, 对PM_2.5浓度进行监控预测, 能够有效地保护大气环境, 进一步地减少空气污染带来的危害. 随着空气质量自动监测站的大范围建立, 由传统的机器学习搭建的空气质量预测模型已经不能满足当今的需求. 本文提出了一种基于多头注意力机制和高斯概率估计的高斯-注意力预测模型, 并对沈阳市某监测站点的数据进行了训练和测试. 该模型考虑了PM_2.5浓度受到其他空气质量数据的影响, 将空气质量数据的分层时间戳(周、日、小时)的信息对齐作为输入, 使用多头注意力机制对于不同子空间的时间序列关联特征进行提取, 能够获得更加完善有效的特征信息, 再经过高斯似然估计得到预测结果. 通过与多种基准模型进行对比, 相较于性能较优的DeepAR, 高斯-注意力预测模型的MSE、MAE分别下降了21%、15%, 有效地提高了预测准确率, 能够较准确地预测出PM_2.5浓度.     

Predicting nitrous oxide emissions with a random-effects model

The main source of anthropogenic N₂O emissions is the application of nitrogen fertilizer to agricultural soils. We present an approach for predicting N₂O emissions based on a statistical random-effects model: the N₂O emission response to applied nitrogen fertilizer is described by an exponential function, the parameters of which are assumed to vary randomly between locations. One of the advantages of this model is that its parameters are easily adjusted to one or several location-specific N₂O measurements. The adjusted model can then be used to predict N₂O emissions for nitrogen fertilizer doses other than those applied at the considered location. We evaluated the accuracy of model prediction, with real and simulated data. The use of location-specific rather than average predictions reduced prediction errors in most cases. Location-specific predictions could be used to estimate background emission in on-farm studies.     

Relationship between motor vehicle emissions of hazardous pollutants,roadway proximity,and ambient concentrations in Portland,Oregon

Previous methodologies for modeling hazardous air pollutant emissions for onroad mobile sources are based on using spatial surrogates to allocate county level emissions to grid cells. A disadvantage of this process is that it spreads onroad emissions throughout a grid cell instead of along actual road locations. Recent air quality modeling in Portland, Oregon, using the CALPUFF dispersion model assigned emissions to specific roadway links. The resulting data were used to develop a regression model to approximate the CALPUFF predicted concentrations, determine the impacts of roadway proximity on ambient concentrations of three hazardous air pollutants, benzene, 1,3-butadiene, and diesel PM, and to estimate the zone of influence around roadways.Independent variables in the model included emission rates and traffic volumes for individual roadway links, distance and direction between roadway links and receptors, and distributions of wind speeds and directions. Dependent variables were derived from simulated annual average pollutant concentrations from motor vehicles at modeled receptor locations, predicted using CALPUFF. The regression model had limited capability to predict CALPUFF concentrations with an R-squared value of about 0.6. The model indicated the zone of influence around a roadway as between 200 and 400 m. The results support the thesis that in order to capture localized impacts of hazardous air pollutants in a dispersion model, there is a need to include individual roadway links.     

Investigating the sensitivity of health benefits to focussed PM2.5 emission abatement strategies

Modelling, pollution monitoring and epidemiological studies all have a role to play in developing effective policies to improve air quality and human health. Epidemiological studies have shown that of particular importance are the effects of fine particulate matter, PM₁₀ and PM_2.5 which can penetrate into human lungs. At present it is not clear which components of PM are responsible for health effects although toxicological studies have identified several potential factors. Hence, based on WHO guidance, current legislation has focused on the total mass, with the EC setting limit values on total PM₁₀, followed by target reductions for population exposure to PM_2.5 in urban agglomerations. Trends in measured concentrations at selected urban monitoring stations are required as evidence for achievement of these reductions. This paper addresses these issues at the borough level in London using the integrated assessment model UKIAM, developed originally for application at the national scale, with illustrations comparing abatement of two contrasting sources – domestic combustion and road transport. The former, dominated by natural gas generating NO_X emissions, contributes to longer range secondary PM formation extending beyond the city. The latter is an important source of black carbon as a primary pollutant causing local exposure, as well as NO_X. WHO data is used in relation to impacts of particle concentrations by mass, and response functions for black carbon are taken from the literature. The results show that from a city perspective there are enhanced benefits from reducing the road transport emissions, especially with regard to potential toxicity of black carbon. The scenarios modelled also highlight the spatial variations of benefits across London, and illustrate deviations from trends as represented by limited monitoring data from the different boroughs, together with the influence upon exposure of mobile population within the city.     

Development of a particle number and particle mass vehicle emissions inventory for an urban fleet

Motor vehicles are major emitters of gaseous and particulate matter pollution in urban areas, and exposure to particulate matter pollution can have serious health effects, ranging from respiratory and cardiovascular disease to mortality. Motor vehicle tailpipe particle emissions span a broad size range from 0.003 to 10 μm, and are measured as different subsets of particle mass concentrations or particle number count. However, no comprehensive inventories currently exist in the international published literature covering this wide size range. This paper presents the first published comprehensive inventory of motor vehicle tailpipe particle emissions covering the full size range of particles emitted. The inventory was developed for urban South-East Queensland by combining two techniques from distinctly different disciplines, from aerosol science and transport modelling. A comprehensive set of particle emission factors were combined with transport modelling, and tailpipe particle emissions were quantified for particle number (ultrafine particles), PM₁, PM_2.5 and PM₁₀ for light and heavy duty vehicles and buses. A second aim of the paper involved using the data derived in this inventory for scenario analyses, to model the particle emission implications of different proportions of passengers travelling in light duty vehicles and buses in the study region, and to derive an estimate of fleet particle emissions in 2026. It was found that heavy duty vehicles (HDVs) in the study region were major emitters of particulate matter pollution, and although they contributed only around 6% of total regional vehicle kilometres travelled, they contributed more than 50% of the region's particle number (ultrafine particles) and PM₁ emissions. With the freight task in the region predicted to double over the next 20 years, this suggests that HDVs need to be a major focus of mitigation efforts. HDVs dominated particle number (ultrafine particles) and PM₁ emissions; and LDV PM_2.5 and PM₁₀ emissions. Buses contributed approximately 1–2% of regional particle emissions.     

The effect of soil moisture and wind speed on aerosol optical thickness retrieval in a desert environment using SEVIRI thermal channels

Dust emission and deposition are associated with several factors such as surface roughness, land cover, soil properties, soil moisture (SM), and wind speed (WS). A combination of land surface and remote-sensing models has recently been investigated for dust detection and monitoring. The thermal bands of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) satellite are widely used for qualitative detection of dust over desert because of their high spectral and temporal resolutions. In this work, the contribution of ground-measured WS data and satellite-measured SM data on aerosol optical thickness (AOT) retrieval was investigated using an artificial neural network (ANN) model. ANNs have been applied in similar applications and have shown a higher performance than simple multiple-regression models. This performance is mainly due to the ANN's ability to capture complex and non-linear relationships between inputs and outputs. A combination of MSG/SEVIRI brightness temperature (BT)/brightness temperature differences (BTDs), BTD_3.9–10.8, BTD_8.7–10.8, BTD_10.8–12, and BT_3.9, was used as input to the base ANN model while Aerosol Robotic Network (AERONET) AOT (level 2) data at 0.5 μm were used as output. These input/output sets were obtained from two stations (Hamim and Mezaira) lying in the inland desert of the United Arab Emirates (UAE). About 3800 observations were collected, of which two-thirds were used to train the ANN model and the remaining third was kept as an independent set to assess the accuracy of the trained model. Later, Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) SM data and ground-measured WS data were used as additional inputs to the base model to investigate their contribution to the AOT retrieval. SM data consist of daytime AMSR-E-derived daily and collected from a National Snow and Ice Data Centre (NSIDC)-archived database. Hourly average WS data were also collected at 10 m height in the same AERONET sites from two stations managed by the UAE National Centre of Meteorology and Seismology. All ground and satellite measurements were extracted for the closest time to AERONET measurements. The use of these additional inputs has been shown to have a positive impact on the accuracy of simulated AOT. The addition of these inputs to the base ANN increased R ² from 0.68 to 0.76 and reduced root mean square error from 0.113 to 0.09.     

Co-active neurofuzzy inference system for evapotranspiration modeling

This study proposes co-active neuro-fuzzy inference system (CANFIS) for daily reference evapotranspiration (ET₀) modeling by using daily atmospheric parameters obtained from California Irrigation Management Information System (CIMIS) database. The CANFIS model is trained and tested using three stations from different geographical locations in California. The model is compared with the well-known conventional ET₀ models such as the CIMIS Penman equation, the Penman–Monteith equation standardized by the Food and Agriculture Organization (FAO-56 PM), the Hargreaves equation and the Turc equation. Meteorological variables; solar radiation, air temperature, relative humidity and wind speed taken from CIMIS database for 4 years (January 2002–December 2005) are used to evaluate the performance analysis of the models. Statistics such as average, standard deviation, minimum and maximum values, as well as criteria such as root mean square error (RMSE), the efficiency coefficient (E) and determination coefficient (R ²) are used to measure the performance of the CANFIS. Considerably well performance is achieved in modeling ET₀ by using CANFIS. It is concluded from the results that CANFIS can be proposed as an alternative ET₀ model to the existing conventional models.     

基于自适应层级图卷积的多站点空气质量预测模型

时空预测任务在污染治理、交通、能源、气象等领域应用广泛. PM_2.5浓度预测作为典型的时空预测任务, 需要对空气质量数据中的时空依赖关系进行分析和利用. 现有时空图神经网络(ST-GNNs)研究所使用的邻接矩阵使用启发式规则预定义, 无法准确表示站点之间的真实关系. 本文提出了一种自适应分层图卷积神经网络(AHGCNN)用于PM_2.5预测. 首先, 引入了一种分层映射图卷积架构, 在不同层级上使用不同的自学习邻接矩阵, 以有效挖掘不同站点之间独特的时空依赖. 其次, 以基于注意力的聚合机制连接上下层邻接矩阵, 加速收敛过程. 最后, 将隐藏的空间状态与门控循环单元相结合, 形成一个统一的预测架构, 同时捕捉多层次的空间依赖关系和时间依赖关系, 提供最终的预测结果. 实验中, 我们与7种主流预测模型进行对比, 结果表明该模型可以有效获取空气监测站点之间的时空依赖, 提高预测精确度.     

基于KNN-LSTM的PM2.5浓度预测模型

目前多数PM_2.5浓度预测模型仅利用单个站点的时间序列数据进行浓度预测, 并没有考虑到空气质量监测站之间的区域关联性, 这会导致预测存在一定的片面性. 本文利用KNN算法选择目标站点所在区域中与其相关的空间因素, 并结合LSTM模型, 提出基于时空特征的KNN-LSTM的PM_2.5浓度预测模型. 以哈尔滨市10个空气质量监测站的污染物数据进行仿真实验, 并将KNN-LSTM模型与其他预测模型进行对比, 结果显示: 模型相较于BP神经网络模型平均绝对误差(MAE)、均方根误差(RMSE)分别降低了19.25%、13.23%; 相较于LSTM模型MAE、RMSE分别降低了4.29%、6.99%. 表明本文所提KNN-LSTM模型能有效提高LSTM模型的预测精度.     

基于社区网络的大气污染源定位算法

污染源定位是大气污染治理与预防中的重要环节.为了避免地表状况、温度和风向等环境条件对污染源定位的影响,提出一种基于社区网络分析的污染源定位算法.通过Granger因果检验方法分析各监测点的空气质量指数AQI的时间序列,得出任意两个监测点的AQI值之间的影响关系.以监测点作为节点,以影响关系作为监测点间的关联关系,构建污...     

网络化数据采集系统在风光互补电厂中的应用

风能、太阳能属于可再生、无污染的清洁能源,风光互补发电系统是利用太阳能和风能的互补性,向负载提供稳定的输出;针对实际风光互补发电厂中各电站较为分散的特点,以及实现对风光互补发电厂的远程监控,运用CAN总线构建了包括各独立风光互补发电站的数据采集点在内的现场总线网络,并利用GPRS网络最终设计了一种新型的集数据采集、控制及远程通信于一体的风光互补发电厂远程监控系统;可对偏远地区无人值守的风光互补发电厂的运行及所获得的电能质量进行有效远程监控。     

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO₂) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO₂ concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.     

Localization of aerosol sources in East-European region by back-trajectory statistics

A concentration-weighted trajectory method for aerosol source localization based on joint statistical analysis of aerosol column volume concentrations and back-trajectory data was used to estimate the spatial distribution of aerosol sources in the East-European region. The aerosol column volume concentration data measured at five AERONET network sites, Belsk, Minsk, Kyiv, Moldova/Kishinev, and Sevastopol, were used. The geographical areas responsible for increased aerosol content at the monitoring sites were mapped separately for coarse-mode and fine-mode aerosol fractions. The investigated area is located between 42° and 62° N in latitude and between 12° and 50° E in longitude.

It was shown that the northeastern territories (in relation to the monitoring stations) give a small contribution to the coarse-mode aerosol content. The events of increased coarse-mode aerosol concentration have been caused by sources in the southeastern regions. On average, the air masses with a large content of coarse-mode aerosol particles were delivered to all stations from regions around Donetsk, Rostov-on-Don, and Kharkiv cities. The fine-mode aerosol fraction originated from areas of Tambov, Voronezh, and Kharkiv cities. The calculated aerosol source regions partly correspond to European Monitoring and Evaluation Programme data for eastern Europe. The cause of difference between calculated regions responsible for increased aerosol content at the monitoring sites and the sources of particle emission according to European Monitoring and Evaluation Programme data are discussed.     

Wind modelling,validation and sensitivity study using Weather Research and Forecasting model in complex terrain

A physically-based wind model is applied to determine wind speed and direction and to conduct a model sensitivity analysis. The focus is the East African site of the Lake Turkana Wind Farm, characterized by complex terrain and high diurnal variability that creates a nocturnal jet of typically 15 m/s. Observations from three tall meteorological masts are compared with Weather Research and Forecast (WRF) model outputs. WRF is configured with four domains nested down to 900 m spatial resolution. The model is tested with initialization fields from two different sources, optimised using different grid configurations and parameterization schemes. Comparing model and data from 3 tall masts A, B and C yields that the primary source of error in WRF model simulation in a complex terrain is due to incorrect specification of boundary fields used to initialize the model. RMSEs achieved in this research are ≤2 m/s representing good model performance (Emery et al., 2001).     

A model to predict land susceptibility to wind erosion in western Queensland,Australia

This paper describes the development and validation of the Australian Land Erodibility Model (AUSLEM), designed to predict land susceptibility to wind erosion in western Queensland, Australia. The model operates at a 5 × 5 km spatial resolution on a daily time-step with inputs of grass and tree cover, soil moisture, soil texture and surficial stone cover. The system was implemented to predict land erodibility, i.e. susceptibility to wind erosion, for the period 1980–1990. Model performance was evaluated using cross-correlation analyses to compare trajectories of mean annual land erodibility at selected locations with trends in wind speed and observational records of dust events and a Dust Storm Index (DSI). The validation was conducted at four spatial length scales from 25 to 150 km using windows to represent potential dust source areas centered on and positioned around eight meteorological stations within the study area. The predicted land erodibility had strong correlations with dust-event frequencies at half of the stations. Poor correlations at the other stations were linked to the inability of the model to account for temporal changes in soil erodibility, and comparing trends in the land erodibility of regions with dust events whose source areas lie outside the regions of interest. The model agreement with dust-event frequency trends was found to vary across spatial scales and was highly dependent on land type characteristics around the stations and on the types of dust events used for validation.