Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO₂) into ozone (O₃) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O₃ in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O₃ concentration was tested. Results indicated that the presence of NO₂ and sunshine influence the concentration of O₃ in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.     

北京首都机场绿色滑行时间估算

采用北京首都机场2014年实际CDM地面放行数据确定航空器的污染物排放量与离场排队飞机数量和落地滑入飞机数量的强关联性,构建包含这两个解释变量为影响因素的多元线性回归模型,用以估算几种常见机型在首都机场地面运行时的最小污染物排放量和绿色滑行时间。对比实际污染物排放量与最小污染物排放量,得出首都机场离场地面污染物排放量远远超过最小污染物排放量。     

On the evaluation of box model results: the case of BOXURB model

In the present paper, the BOXURB model results, as they occurred in the Greater Area of Athens after model application on an hourly basis for the 10-year period 1995-2004, are evaluated both in time and space in the light of observed pollutant concentrations time series from 17 monitoring stations. The evaluation is performed at a total, monthly, daily and hourly scale. The analysis also includes evaluation of the model performance with regard to the meteorological parameters. Finally, the model is evaluated as an air quality forecasting and urban planning tool. Given the simplicity of the model and the complexity of the area topography, the model results are found to be in good agreement with the measured pollutant concentrations, especially in the heavy traffic stations. Therefore, the model can be used for regulatory purposes by authorities for time-efficient, simple and reliable estimation of air pollution levels within city boundaries.     

动态因子分析在环境监测数据综合处理中的应用

动态因子分析(DFA)作为降维度的多元统计方法,被设计用于时间序列分析,以揭示多元变量中解释变量与共同趋势对响应变量的影响程度。相较传统多元统计方法,DFA显性考虑时间因素,并量化影响响应变量的潜在因素。该方法可以忽略内在理化过程,具有良好的模拟效果,且在国外已广泛应用于地表水及地下水生态环境、空气污染等领域,但在国内尚未被应用。DFA在自身完善、应用范围、信息挖掘、数据预处理、预测分析、空间分析方面仍有巨大发展前景。     

Estimation of Ozone Trend in Central Greece, Based on Meteorologically Adjusted Time Series

Data referring to an approximately 8-year period (1999–2007) are analyzed in order to estimate the trend of the daily maximum hourly value of ozone concentration at the east coast of central Greece, where the summer background ozone concentration is high. A Kolmogorov–Zurbenko filter is applied to remove the short-term component from the raw time series of ozone and meteorological variables. Regression models are developed in order to produce meteorologically adjusted ozone time series, involving the noise-free temperature, relative humidity, and wind speed as independent variables. The analysis verifies that the meteorological adjustment provides better results on estimating ozone’s trend, which is found to be increasing (α?=?0.001) with an annual rate of 1.34?±?0.07?μg/m³. This trend could mainly be attributed to policy and changes in the emissions of ozone’s precursors. Additionally, the short-term component of ozone concentration is also meteorologically adjusted and its impact on the trend is examined. The analysis shows that its contribution is of minor importance when the ozone trend is adjusted by temperature, relative humidity, and wind speed. Moreover, the sea breeze circulation system that is frequently developed in the area influences the short-term and seasonal ozone variation, and therefore, it should be taken into account when producing meteorologically adjusted time series. The study’s conclusions could be exploited by environmental and agricultural authorities in order to develop their long-term strategies towards the air quality management.     

Input strategy analysis for an air quality data modelling procedure at a local scale based on neural network

In recent years, a significant part of the studies on air pollutants has been devoted to improve statistical techniques for forecasting the values of their concentrations in the atmosphere. Reliable predictions of pollutant trends are essential not only for setting up preventive measures able to avoid risks for human health but also for helping stakeholders to take decision about traffic limitations. In this paper, we present an operating procedure, including both pollutant concentration measurements (CO, SO₂, NO₂, O₃, PM10) and meteorological parameters (hourly data of atmospheric pressure, relative humidity, wind speed), which improves the simple use of neural network for the prediction of pollutant concentration trends by means of the integration of multivariate statistical analysis. In particular, we used principal component analysis in order to define an unconstrained mix of variables able to improve the performance of the model. The developed procedure is particularly suitable for characterizing the investigated phenomena at a local scale.     

Comparing the Number of Ozone Exceedances in Different Seasons of the Year in Mexico City

Ozone air pollution is a serious problem in several cities of the world. Hence, to analyse the behaviour of this pollutant is a very important issue. One problem of interest is to study the behaviour of the inter-occurrences times between two ozone exceedances, i.e. between two days in which the pollutant’s measurement surpasses a given threshold. Another interest resides in comparing the behaviour of ozone measurements in different seasons of the year. In this paper we use some Poisson models to analyse this problem. The time interval at which the ozone measurements were taken is split into subintervals corresponding roughly to the seasons of the year. We consider three parametric forms for the mean of the Poisson model, and consequently for the mean of the inter-occurrences times. In each model, the parameters describing its mean are estimated using Bayesian inference via Markov chain Monte Carlo methods. The models are applied to the ozone measurements provided by the Mexico City monitoring network. Theoretical results suggest that an increase has occurred in the mean inter-exceedances times and this is corroborated by the observed data. Differences between the behaviour of the pollutant during different seasons of the year are also detected as well as similarities in the same season in different years. Besides estimating the mean of the Poisson models, inference for the possible presence and location of change-points indicating change of parameters of the model is also performed.     

大连市臭氧污染特征及典型污染日成因

通过对大连市区10个空气监测子站的监测数据进行分析,探讨了大连市臭氧污染的时空分布、气象条件对臭氧污染的影响,对臭氧污染日进行了归类分析。结果表明,大连市臭氧污染主要出现在4—10月。在强紫外辐射、高温、低湿、低压和低风速的气象条件下,监测点位的臭氧浓度较高。臭氧污染日的日变化分为单峰型、双峰型和夜间持续升高型3种类型。通过对2015年的一次高浓度臭氧污染过程的气象条件、污染物浓度和污染气团轨迹进行分析,发现臭氧浓度在夜间持续升高现象与区域输送密切相关。     

Near-surface ozone levels and trends at rural stations in France over the 1995–2003 period

There is a considerable interest in quantifying near-surface ozone concentrations and associated trends, as they serve to define the impacts on ozone of the anthropogenic precursors reductions and to evaluate the effects of emission control strategies. A statistical test has been used to the ozone air concentrations measured in the French rural monitoring network stations, called MERA, in order to bring out spatio-temporal trends in air quality in France over the 1995-2003 period. The non-parametric Mann-Kendall test has been developed for detecting and estimating monotonic trends in the time series and applied in our study at annual values: mean, 98th percentile and median based on hourly averaged ozone concentrations and applied to daily maxima. In France, when averaged overall 9 stations between 1995 and 2003, a slight increasing trend of the O(3) levels (+0.6 +/- 1.3% year( - 1)) is observed, which is strongly influenced by the concentrations of the high altitude stations. In stations below 1000 m a mean rate of -0.48% year( - 1) from annual average concentrations, of -0.45% year( - 1) for medians and of +0.56% year( - 1) for P.98 over the 1995-2003 period were obtained. In stations above 1,000 m a mean rate of +1.75% year( - 1) from annual averages values, of +4.05% year( - 1) for medians and of +2.55% year( - 1) for P.98 were calculated over the 1997-2003 period. This situation is comparable to the one observed in other countries. In Europe and in France a reduction of precursor emissions is observed whereas a slight increasing trend of the O(3) levels is observed over the 1995-2003 period. One reason is the non-linearity of chemical ozone production with respect to precursor emissions. Possible explanations are an increase in near-surface ozone values caused by a reduced ozone titration by reduced NO( x ), the meteorological parameters change, an increase in bio-geogenic compound concentrations, the intercontinental transport from North America and Asia and the influence of stratospheric-tropospheric exchanges. These possible explanations must be interpreted carefully as on the short time scales considered.     

Grey Box and Component Models to Forecast Ozone Episodes: A Comparison Study

For the purpose of short-term forecasting of high ozone concentration episodes stochastic models have been suggested and developed in the literature. The present paper compares the quality of forecasts produced by a grey box and a component time-series model. The summer ozone patterns for three European urban areas (two continental and one mediterranean) are processed. By means of forecast performance indices according to EC and WHO guidelines, the following features of the models could be found: The grey box model is highly adaptive and produces forecasts with low error variance that increases with the time horizon of forecast. The component model is more 'stiff' that results in a higher forecast-error variance and poorer adaption in detail. The forecast horizon, however, could be enlarged with this model. The accuracy of predicting threshold exceedance is similar for both models. This can be understood from the assumption of a cyclical time development of ozone that was made for both models.     

海口市臭氧污染特征

基于2013—2015年海口市4个空气质量自动监测站点数据,结合气象资料,分析了海口市O_3的污染特征。结果表明:海口市O_3总体优良,优良天数比例为99.4%,污染天数均为轻度污染;在良和污染天数中,O_3作为首要污染物的天数占40%,超过其他5项污染物占比。海口市10月O_3浓度最高。O_3月均浓度与温度呈负相关关系,同时与风向有密切关系:5—8月气温较高,以南风为主,O_3浓度较低;1月北风频率较高,易受外来污染传输作用,O_3浓度相对较高。O_3超标日以东北风为主,日变化并未呈现单峰型特征,12:00—22:00时段O_3浓度在10%范围内小幅变化。台风外围型和北方冷高压底部型是造成海口市O_3超标的2类典型天气形势。     

Life cycle assessment of solar photo-Fenton and solar photoelectro-Fenton processes used for the degradation of aqueous α-methylphenylglycine

A comparative Life Cycle Assessment (LCA) of solar photo-Fenton and solar photoelectro-Fenton, two solar-driven advanced oxidation processes (AOPs) devoted to the removal of non-biodegradable pollutants in water, is performed. The study is based on the removal, at laboratory scale, of the amino acid α-methylphenylglycine, a good example of soluble and non-biodegradable target pollutant. The system under study includes chemicals, electricity, transport of all raw materials to the plant site, and the generation of emissions, but it does not take into account the impact of the infrastructure needed to build a hypothetical solar plant. Nine environmental impact categories are included in the LCA: global warming potential, ozone depletion potential, aquatic eutrophication potential, acidification potential, human toxicity potential, photochemical ozone formation potential, fresh water aquatic ecotoxicity potential, marine aquatic ecotoxicity potential, and terrestrial ecotoxicity potential and abiotic resource depletion potential. Although previous experimental results show that both AOPs are able to efficiently degrade the pollutant, the LCA indicates that solar-driven photo-Fenton is the most environmentally friendly alternative, mainly because the use of electricity in solar photoelectro-Fenton experiments involves high environmental impacts.     

衡阳市区和衡山背景站臭氧浓度变化规律的对比分析

选取衡阳市区和衡山背景站臭氧自动监测数据,分析两地的臭氧污染特征。对空气质量的优良率情况、臭氧作为首要污染物的变化情况、臭氧浓度的日变化特征、典型时段的浓度变化特征、臭氧浓度的月际变化特征和臭氧与PM_(2.5)的关联情况等进行了分析。结果表明,多云及阴雨天气时,衡阳市区的臭氧浓度日变化幅度大于衡山背景站。夏季,衡阳市区和衡山背景站的臭氧浓度的日变化特征规律差异较大,臭氧浓度分布比较分散,前者为典型的单峰形,后者则波动平缓。冬季,日变化幅度不大,但衡阳市区的臭氧浓度明显低于衡山背景站。衡山背景站和衡阳市区的臭氧基本同步变化,但日均值高于衡阳市区。     

基于OPAQ的城市空气质量预报系统研究

空气质量预测在国内的关注度日益提高,传统的空气质量预测系统通常运用数值化学传输模型,利用物理方程来计算污染物的扩散、沉降和化学反应。而化学传输模型的预测准确性很大程度上需要依赖详细的污染源排放信息和气象模型的输出结果。基于统计模型的OPAQ空气质量预报业务系统,采用人工神经网络算法,可预测各污染物的日均值或日最大值。并对北京空气质量预报的结果进行了评价,OPAQ空气质量预报业务系统对空气质量预测的准确性较高,能够利用较低的计算资源得到较为准确的预测结果。与数值预报相比,OPAQ空气质量预报业务系统不需要大量的基础数据作为输入,可弥补数值预报的不足,并成为数值预报的有力补充。     

A comparative study on various statistical techniques predicting ozone concentrations: implications to environmental management

The objective of the present work is to compare various techniques for modeling the dependence of the tropospheric ozone concentrations on several meteorological and pollutant parameters. The study focuses on two different sites in the metropolitan area of Athens, Greece; one in the city centre and another one in the suburbs. It is found that although simple Linear Regression Analysis fails to construct accurate equations due to the existence of multicollinearity among the independent variables, still various combinations of a Multivariate Method (PCA) and Stepwise Regression Analysis manage to produce equations free of the multicollinearity issue. The derived formulas are validated and prove to have R(2) values in the order of 0.8 approximately. However, the equations are found to be unsuccessful in case of severe episodes. For this reason, a new procedure is followed for estimating the ozone values in case of episodes exclusively. The new R(2) value is estimated to be 0.9, approximately.     

海口市臭氧污染及气象条件特征

近年来,臭氧已成为许多城市环境空气的主要污染物之一。笔者分析了2020年海口市5个不同方位代表性监测站点逐小时空气质量监测数据及对应站点的气象要素监测数据。研究结果表明:海口市2020年环境空气污染程度为三级以上的天数有11d,其首要污染物均为臭氧。臭氧浓度高值时段主要出现在10-12月。浓度最大值主要出现在每日14:00-17:00,最小值出现在每日05:00-08:00。气象要素日均值与臭氧浓度相关性大小依次为最高温度＞平均温度＞相对湿度＞降水量＞日照时数＞风速。台风外围下沉气流和东北气流的共同影响是导致海口市臭氧浓度超标的主要因素,下沉气流更有利于低层大气中臭氧的堆积,同时在东北气流影响下,上游区域污染物的传输也会导致海口市臭氧浓度增加。     

佛山市近地面臭氧污染特征及相关气象因子分析

利用2014年佛山市8个国控大气自动监测点位的O_3监测数据,分析了佛山市的O_3污染特征,结果表明,2014年O_3日最大8 h平均值的第90百分位数为167μg/m~3,O_3为首要污染物的超标天数为43d,占比46.7%;ρ(O_3)区域变化不大;ρ(O_3)月变化呈现"三峰型",全年高ρ(O_3)集中在6—10月份,其中7月份出现全年最高峰值;ρ(O_3)日变化呈单峰型分布,夜间浓度较低且变化平缓,14:00—16:00左右达到峰值,并存在一定的"周末效应",但并不明显;ρ(O_3)与气温呈显著正相关,与湿度、气压、雨量呈显著负相关,与风向、风速的相关性相对较弱;总体上看,高温、低湿、微风、偏南风、低压、无雨的天气条件下高ρ(O_3)更容易出现。     

ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya

The present study explores for the first time the possibility of modelling sediment concentration with artificial neural networks (ANNs) at Gangotri, the source of Bhagirathi River in the Himalaya. Discharge, rainfall and temperature have been considered as the main controlling factors of variations in sediment concentration in the dynamic glacial environment of Gangotri. Fourteen feed forward neural networks with error back propagation algorithm have been created, trained and tested for prediction of sediment concentration. Seven models (T1-T7) have been trained and tested in the non-updating mode whereas remaining seven models (T1a-T7a) have been trained in the updating mode. The non-updating mode refers to the scenario where antecedent time (previous time step) values are not used as input to the model. In case of the updating mode, antecedent time values are used as network inputs. The inputs applied in the models are either the variables mentioned above as individual factors (single input networks) or a combination of them (multi-input networks). The suitability of employing antecedent time-step values as network inputs has hence been checked by comparative analysis of model performance in the two modes. The simple feed forward network has been improvised with a series parallel non-linear autoregressive with exogenous input (NARX) architecture wherein true values of sediment concentration have been fed as input during training. In the glacial scenario of Gangotri, maximum sediment movement takes place during the melt period (May–October). Hence, daily data of discharge, rainfall, temperature and sediment concentration for five consecutive melt periods (May–October, 2000–2004) have been used for modelling. High Coefficient of determination values [0.77–0.88] have been obtained between observed and ANN-predicted values of sediment concentration. The study has brought out relationships between variables that are not reflected in normal statistical analysis. A strong rainfall: sediment concentration and temperature: sediment concentration relationship is shown by the models which are not reflected in statistical correlation. It has also been observed that usage of antecedent time-step values as network inputs does not necessarily lead to improvement in model performance.