Pollutant Concentration Values Research Articles

Applying machine learning techniques in air quality prediction

Air pollution levels become more and more dangerous every year, which is one оf the most significant and important problems for humanity nowadays. Of all air pollutants, particulate matter has the most harmful effect on the human health, especially in the long term. This article discusses the need to predict changes in PM2.5 concentrations for air quality monitoring and preventing unsafe and risky situations. In the course of the presented study, detailed visualization of the original data and а comparative survey of the application of machine learning methods were carried out. For solving the problem of forecasting time series of particulate matter concentration values several machine learning models, such as ARIMA, Facebook Prophet, and LSTM, were used. At the moment, it is possible to predict the trend of changes in the mean values of pollutant concentrations for several months in advance. But widespread use of these technologies requires more correct data to make better and more accurate predictions.

Analysis of the Nicolaus Copernicus Airport Activity in Terms of the Flight Operations Impact on Air Pollution

The dynamic development of aviation is associated with many benefits, but also, unfortunately, with negative effects. One of the adverse consequences is the exhaust emissions that have a negative impact on human health. It particularly affects the residents of areas neighboring airports, as airport activity deteriorates local air quality. Using the Emissions and Dispersion Modeling System, the activity of the Nicolaus Copernicus Airport was assessed in terms of the flight operations’ contribution to air contamination in the area adjacent to the airport. Emissions from three sources were compared: aircraft, ground support equipment and auxiliary power units. The concentrations of pollutants in inhabited areas located in three different directions in relation to the airport were also estimated. In addition, the effect of distance from the airport on contaminant concentrations was assessed as a function of wind direction. It was noticed that small values of pollutant concentrations, originating from airport activity, appeared within a few kilometers from the airport, even if the prevailing wind direction on a given day was opposite to the analyzed dispersion direction.

Low-Cost Sensors for Air Quality Monitoring - the Current State of the Technology and a Use Overview

Abstract In recent years the monitoring of air quality using cheap sensors has become an interesting alternative to conventional analytical techniques. Apart from vast price differences conventional techniques need to be performed by the trained personnel of commercial or research laboratories. Sensors capable of measuring dust, ozone, nitrogen and sulphur oxides, or other air pollutants are relatively simple electronic devices, which are comparable in size to a mobile phone. They provide the general public with the possibility to monitor air quality which can contribute to various projects that differ in regional scale, commercial funding or community-base. In connection with the low price of sensors arises the question of the quality of measured data. This issue is addressed by a number of studies focused on comparing the sensor data with the data of reference measurements. Sensory measurement is influenced by the monitored analyte, type and design of the particular sensor, as well as by the measurement conditions. Currently sensor networks serve as an additional source of information to the network of air quality monitoring stations, where the density of the network provides concentration trends in the area that may exceed specific measured values of pollutant concentrations and low uncertainty of reference measurements. The constant development of all types of sensors is leading to improvements and the difference in data quality between sensors and conventional monitoring techniques may be reduced.

Effect of wind velocity and flare height parameters on pollution dispersion from one flare with zonal method

With increasing growth of industrial units in developing countries and pollutants produced by these units, nowadays distribution and dispersion modelling of atmosphere pollutants especially in urban areas is an inevitable importance. Dispersion modelling of atmosphere pollutants is a methodology to estimate focus and concentration values of pollutants related to emission source in different seasons. In current study, using numerical analysis, a thermal diffusion of flare and focus values of industrial pollutants simulation using air zonal methodology has been presented. After studying pollutants emission in open area, validation has been done using laboratory data results and computational fluid dynamics method. The results of this study indicate the ability of presented air zonal methodology to predict thermal diffusion of flare and distribution concentration of pollutant source and information gained from this analysis. Then an exploration of effective parameters in pollution emission such as wind velocity, flare height, and pollution emission rate in downstream has been done. As the results show, when wind velocity rises by 130%, pollution will reach far away from production source and with increase in flare height by 25%, the pollution concentration values on the ground has been reduced by 44%. Also with addition of barrier in pollution dispersion path, pollution level will increase by 60%.

Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature.

Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.

Анализ эффективности постадийной очистки производственно-дождевых сточных вод при эксплуатации объектов магистральных нефте- и нефтепродуктопроводов

Treatment of rainwater industrial wastewater during the operation of oil pipelines and petroleum products’ pipelines is a complex technological process that involves water purification from dissolved and emulsified oil products, oil, suspended solids and other polluting components. Organizations of the Transneft system have implemented a highly efficient technology for treatment of oily effluents, which makes it possible to reduce the level of pollutants to standard values. In order to assess the efficiency of the treatment facilities, an industrial, environmental and analytical control has been organized, which is carried out at all stages of the wastewater treatment and disinfection (after each block of equipment). Also, in order to optimize the performance of this procedure, a specialized program has been developed that allows for an operational analysis of the control results, as well as a comparative analysis of data (values of pollutant concentrations and treatment efficiency) at each stage of the process. According to the measurement results, the average indicators of the concentration of pollutants at all stages of purification are calculated for the observation period. According to these indicators, graphs of their compliance with regulatory requirements and comparison graphs with the typical scheme are plotted, and based on the results obtained, a conclusion is drawn about the efficiency of the treatment facilities for the selected observation period and based on the annual results.

Cumulative exposure to organic pollutants of French children assessed by hair analysis

Children represent one of the most vulnerable parts of the population regarding the effects of pollutants exposure on health. In this study, hair samples were collected between October 2013 and August 2015 from 142 French children originating from different geographical areas (urban and rural) and analysed with a GC/MS-MS method, allowing for the detection of 55 biomarkers for pesticides and metabolites both persistent and non-persistent from different families, including: organochlorines, organophosphates, pyrethroids, azoles, dinitroanilines, oxadiazines, phenylpyrazoles and carboxamidas; 4 polychlorobiphenyls (PCBs) and 5 polybromodiphenylethers (PBDEs). The number of compounds detected in each sample ranged from 9 up to 37 (21 on average), which clearly highlighted the cumulative exposure of the children. The results also showed a wide range of concentration of the pollutants in hair (often more than 100 times higher in the most exposed child compared to the less exposed), suggesting significant disparities in the exposure level, even in children living in the same area. In addition to the detection of currently used chemicals, the presence of persistent organic pollutants (POPs) in children also suggests that the French population is still exposed to POPs nowadays. PCP, DEP, PNP, 3Me4NP, trans-Cl2CA, 3PBA, fipronil and fipronil sulfone, presented statistically significant higher concentration in the hair of boys compared to girls. PCP, PNP and 3Me4NP presented statistically significant higher concentration in younger children. Finally, this study also suggests that local environmental contamination would not be the main source of exposure, and that individual specificities (habits, diet…) would be the main contributors to the exposure to the pollutants analysed here. The present study strongly supports the relevance of hair for the biomonitoring of exposure and provides the first values of organic pollutant concentration in the hair of French children.

An artificial neural network ensemble approach to generate air pollution maps.

The objective of this research is to propose an artificial neural network (ANN) ensemble in order to estimate the hourly NO2 concentration at unsampled locations. Spatial interpolation methods and linear regression models with regularization have been compared to perform the ensemble. The study case is based on the region of the Bay of Algeciras (Spain). This area is very industrialized and presents high concentrations of traffic. Air pollution data has been collected from the monitoring network maintained by the Andalusian Government in the region. On one hand, two totally different methods have been used and compared such as inverse distance weight (IDW) and least absolute shrinkage and selection operator (LASSO) in order to generate maps of pollutant concentration values. On the other hand, an ensemble approach has been developed using the outputs of the previous models. The ensemble is based on an ANN with backpropagation learning. An experimental procedure using cross-validation has been applied in order to compare the different models based on several performance indexes (R correlation coefficient, MSE, MAE and d index of fitness) and together to Friedman test and Bonferroni correction. The results reveal that the proposed ensemble approach presents better performance than single models in general terms. A validation procedure has been conducted using a leave-one-out strategy using each monitoring station. IDW method presents an average value of R equals 0.72 and a maximum R equals 0.87, a minimum MSE equals 78.00, a minimum MAE equals 5.841 and a maximum d equals 0.913. LASSO presents an average value of R equals 0.76 and a maximum R equals 0.86, a minimum MSE equals 59.13, a minimum MAE equals 5.490 and a maximum d equals 0.900. Finally, the ANN ensemble shows an average value of R equals 0.77 and a maximum R equals 0.87, a minimum MSE equals 54.05, a minimum MAE equals 4.972 and a maximum d equals 0.915. The main objective has been to produce adequate atmospheric pollutant concentration maps and, therefore, to obtain estimations for locations that are distinct to the monitoring stations. Another objective has been to have in hand a system to produce robust measurements. This kind of system could be useful for missing data imputation and to find out reading errors (i.e. unexpected deviations or calibration problems) in some of the nodes of a network.

Study on influence of viaduct and noise barriers on the particulate matter dispersion in street canyons by CFD modeling

This paper investigates the influence of viaduct, noise barrier, and the ratio of leeward and windward building height (H1/H2) on flow regime and particulate matters (PM) dispersion in an urban street canyon, using Computational Fluid Dynamic (CFD) simulation. Results show that viaduct introduces more sources of particulate and influences the flow pattern significantly. New vortexes form when noise barriers set up along the viaduct and influence the flow around the viaduct and distribution of pollutants at the center of the canyon but influence to a less extent in the key breathing zones. When the street canyon is symmetric, barriers can reduce the peak value of pollutant concentration at buildings wall, and lower the integral concentration in zones that affect people's breathing. When the street canyon is a step-up type (the leeward building is lower than the windward one), barriers affect to a less extent the PM distributions at buildings wall and breathing zones. Increase of the height of windward buildings hinders the diffusion of pollutants. Furthermore, people living in the windward building or walking on the street have to suffer more from the pollutant. When the street canyon is a step-down type (the leeward building is higher than the windward one), H1/H2 = 6/5, pollutants still gather at the leeward wall. It has the least integral pollutant concentration in the zone near buildings and street. H1/H2 = 2, pollutants are transported to the windward wall and accumulated because of the upper main vortex in the canyon. Pedestrians' breathing zone is extremely high compared with other configurations.

Correlation Analysis between Land Use/Cover Change and Air Pollutants—A Case Study in Wuyishan City

Land use changes have significantly altered the natural environment in which humans live. In urban areas, diminishing air quality poses a large threat to human health. In order to investigate the relationship between land use/cover change (LUCC) and air pollutants of Wuyishan City between 2014–2017, an integrated approach was used by combining remote sensing techniques with a landscape ecology methods. Annual, seasonal, and weekly mean values of air pollutant (SO2, NO2, CO, PM10, O3, PM2.5, black carbon) concentration and atmospheric visibility were calculated to develop a Pearson correlation between LUCC and air pollutants concentration. Results showed an increase in forested areas (1.79%) and water areas (15.89%), with a simultaneous reduction in cultivated land (6.47%), bare land (72.61%), and built-up land (16.03%) from 2014 to 2017. The transition matrix of land use types revealed that (i) forest expansion took place mainly at the expense of cultivated land (13.94%) and bare land (27.48%); and (ii) water area expansion took place mainly at the expense of cultivated land (1.29%) and forests (0.21%). In 2017, the proportion of days with AQI level I (94.52%) was higher than that in 2014 (88.77%). Additionally, the annual average visibility in 2017 (37.42 km) was higher than 2014 (27.46 km). The concentration of SO2, CO, O3, and black carbon was positively correlated with the cultivated land. The concentration of SO2, CO, and black carbon negatively correlated with the increase of forests. PM10, and PM2.5 is negatively correlated with the water area. Visibility was found to be positively correlated with forested area, and negatively correlated with cultivated land. The findings from this study represent a valuable gain in understanding of policies aimed at improving, safeguarding, and monitoring air quality. These results can be used to inform land-use planning decisions in a comprehensive way and could be a valuable tool for LUCC rational management strategies.

МОДЕЛИРОВАНИЕ РАСПРОСТРАНЕНИЯ В АТМОСФЕРЕ ЗАГРЯЗНЯЮЩИХ ВЕЩЕСТВ ВЫБРОСОВ ЭЛЕКТРОСТАНЦИЙ НА БАЗЕ ПРОГРАММНОГО КОМПЛЕКСА «SKAT»

Актуальность рассматриваемой тематики заключается в необходимости повышения точности прогнозирования содержания загрязняющих веществ в атмосферном воздухе от выбросов объектов энергетики, в том числе атомных станций, при проведении независимой оценки. Цель работы: обоснование выбора методики расчета рассеивания газообразных загрязняющих веществ от непрерывно действующих высоких источников с учетом метеорологических факторов и тестирование опыта эксплуатации программного комплекса. Объекты: современные методы расчетов рассеивания выбросов загрязняющих веществ в атмосферном воздухе, рекомендуемые для использования на территории России и методика Пасквилла для расчетов загрязнения от непрерывно действующего высокого источника при различных стационарных метеорологических условиях. Методы: математическое моделирование и расчетный анализ на основе разрабатываемого компьютерного программного комплекса «SKAT». Результаты. Проведен анализ методов по рассеиванию газообразных загрязняющих веществ в атмосфере: МРР-2017 и Пасквилла. Сделан вывод о том, что для расчетов рассеивания газообразных загрязняющих веществ выброса объектов энергетики для различных категорий состояния атмосферы в условиях постоянства метеорологических факторов предпочтительнее использовать методику Пасквилла, основанную на модели Гаусса. Разработанный программный комплекс «SKAT» может быть использован для прогнозирования приземных концентраций загрязняющих веществ и расчетов рассеивания загрязняющих веществ в атмосферном воздухе при решении задач мониторинга выбросов загрязняющих веществ в условиях действующих объектов. Применительно к условиям г. Северска Томской области для проектируемого энергоблока с реактором БРЕСТ-300 и пристанционным ядерным топливным циклом по модели Гаусса проведены расчеты распределения выбросов радионуклидов в приземном слое атмосферы.

High resolution annual average air pollution concentration maps for the Netherlands

Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO2, NOx, PM2.5, PM2.5absorbance and PM10. The raster datasets on 5×5 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals.

Detection of outliers in pollutant emissions from the Soto de Ribera coal-fired power plant using functional data analysis: a case study in northern Spain.

For more than a century, air pollution has been one of the most important environmental problems in cities. Pollution is a threat to human health and is responsible for many deaths every year all over the world. This paper deals with the topic of functional outlier detection. Functional analysis is a novel mathematical tool employed for the recognition of outliers. This methodology is applied here to the emissions of a coal-fired power plant. This research uses two different methods, called functional high-density region (HDR) boxplot and functional bagplot. Please note that functional bagplots were developed using bivariate bagplots as a starting point. Indeed, they are applied to the first two robust principal component scores. Both methodologies were applied for the detection of outliers in the time pollutant emission curves that were built using, as inputs, the discrete information available from an air quality monitoring data record station and the subsequent smoothing of this dataset for each pollutant. In this research, both new methodologies are tested to detect outliers in pollutant emissions performed over a long period of time in an urban area. These pollutant emissions have been treated in order to use them as vectors whose components are pollutant concentration values for each observation made. Note that although the recording of pollutant emissions is made in a discrete way, these methodologies use pollutants as curves, identifying the outliers by a comparison of curves rather than vectors. Then, the concept of outlier goes from being a point to a curve that employs the functional depth as the indicator of curve distance. In this study, it is applied to the detection of outliers in pollutant emissions from a coal-fired power plant located on the outskirts of the city of Oviedo, located in the north of Spain and capital of the Principality of Asturias. Also, strengths of the functional methods are explained.

Об одной оптимизационной задаче регулирования мощности источников выбросов и минимизации экологического ущерба

The modeling of the impurities transportation makes it possible to solve the problems of blocking and neutralizing pollution, local assessment of the environmental state and forecasting of possible consequences of anthropogenic loads on the ecosystem. To solve the optimization problems concerning regulation of the impurities emission power from sources and aimed at minimization of damage to the environment, a mathematical model has been developed, which includes submodels: hydrodynamic, transfer and optimization for controlling the power of emissions. We study the initial boundary value problem for the balance equation of impurities emitted into the water medium by a concentrated source, taking into account the degradation of the components of a multicomponent pollutants using the turbulent diffusion equation. The paper considers both stationary and moving emission sources. When studying the sedimentation process, the possible heterogeneity of the underlying surface of the bottom is taken into account, which means the presence of mixed boundary conditions. To study the nature of interactions in the system operating under the influence of natural and anthropogenic factors, during the assessment of environmental vulnerability to various impacts, the adjoint problems are used when the object of study is not the pollutant concentration field itself, but some of its functionals. Among these functionals are: the average annual concentration of various pollutants, the total amount of sedimented impurities. Discrete approximations and the computational algorithm scheme are constructed on the basis of the variational principle using the splitting method. The result of the numerical implementation of the model described by the direct problem is the pollutant concentration values calculated with a given accuracy. The numerical solution of conjugated problems assumes the consistency of finite-difference approximations of the direct and conjugated problems. In this case, the structure of algorithms for solving a direct problem determines the structure of algorithms for the conjugated problem, which is solved with the reverse order of the splitting stages in the opposite direction of time. Numerical models of transportation for single-component and multicomponent pollutant emitted by point and area sources are constructed, taking into account their degradation and interaction with the underlying surface at the bottom. They can be used to solve problems related to the assessment of the current state and prospects of the region.

Prediction of groundwater environmental impact on a power plant under accident conditions

According to the engineering features of the proposed power plant and the possible pollution accidents, the accident conditions of two kinds of polluted groundwater, such as the light diesel oil tank bottom leakage and the power plant pond bottom leakage are set up. Using the two-dimensional hydrodynamic dispersion equation of the continuous injection and instantaneous injection, the groundwater pollution in the accident condition is predicted and evaluated. The results show that, with the passage of time, the migration range of groundwater pollutants caused by sewage leakage from industrial wastewater buffer tank shows a trend of gradual expansion; however, the maximum value of pollutant concentration exceeding standard is gradually decreasing. After 10 years, the excessive pollutant migration distance has reached 390 m. If no remedial measures are taken in time, during the operation of the power plant, industrial wastewater pool sewage leakage will cause long-term pollution to the groundwater. Similarly, if the accident was diesel tank explosion, the range of diesel contamination to groundwater is also increasing over time, but the concentration of pollutants is gradually decreasing. About 25 years later, the pollutant concentration within 400 m downstream of pollution source has decreased to 0.04 mg/L, and the impact of diesel oil leakage on groundwater has largely disappeared.

Interference Adsorption Mechanisms of Dimethoate, Metalaxyl, Atrazine, Malathion and Prometryn in a Sediment System Containing Coexisting Pesticides/Heavy Metals Based on Fractional Factor Design(Resolution V) Assisted by 2D-QSAR

The mechanisms of adsorption of pesticides(dimethoate, metalaxyl, atrazine, malathion and prometryn) and heavy metals(Cu, Cd, Pb, Zn and Ni) coexisting in sediments, with pesticides as target pollutants, and the influence of their main effects and double-order interaction effects were studied using the experimental design module in the Minitab software package with a 210‒3 fractional factorial design method at resolution V. The main, double-order interaction, synergistic and antagonistic effect values of pollutant concentrations influencing the adsorption of pesticides were set as dependent variables, while various quantum chemical parameters of pesticides were set as independent variables, and two-dimensional quantitative structure activity relationship(2D-QSAR) models were established by stepwise regression to reveal the adsorption mechanisms of pesticides in a composite contamination system. The main effects of pollutants concentration played the primary role in the adsorption of dimethoate and malathion(the rates of contributions were 53.54% and 56.46%, respectively), while double-order interaction effects were primarily responsible for metalaxyl, atrazine and prometryn adsorption(the rates of contributions were 79.05%, 60.21% and 57.89%, respectively) in the pesticide/heavy metals coexisting sediment system. The synergistic effects of the main effects and double-order interaction effects of pollutants concentration(synergistic effects) played a leading role in adsorption of malathion and prometryn(the rates of contributions were 70.61% and 69.61%, respectively), while antagonistic effects of the main effects and double-order interaction effects of pollutants(antagonistic effects) played a dominant role in the adsorption of dimethoate, metalaxyl and atrazine(the rates of contributions were 58.82%, 56.89% and 58.24%, respectively). Moreover, the correlation coefficient value(R2) ranged from 0.986 to 0.999(>0.8783) in the 2D-QSAR model, while the standard deviation(SD) ranged from 0.006 to 0.066 and the F test values were 22.684―199.544, indicating the model has good predictive ability and fit. The 2D-QSAR model revealed a significant correlation(P=0.05) between the main effects of pollutants concentrations on pesticides adsorption(main effect values) and the most positive hydrogen atomic charge( $$q_{H^+}$$ ), the highest occupied molecular orbital energy(EHOMO) and the dipole moment(μ). Furthermore, double-order interaction effect values of pollutant concentrations influenced the adsorption of pesticides(double-order interaction effect values), and the most positive atomic charge(q+), $$q_{H^+}$$ , and the lowest occupied molecular orbital energy(ELUMO) were significantly correlated. The qH+, ELUMO and μ of pesticides were found to be significant factors promoting pesticides adsorption, while the q+ and ELUMO of pesticides were significant inhibiting factors(P=0.05). Overall, this study provides a theoretical basis for further realization of combined pollution control of pesticide pollutants in complex environmental systems.

Quantifying nonpoint source emissions and their water quality responses in a complex catchment: A case study of a typical urban-rural mixed catchment

As two key threats to receiving water bodies, the generation mechanisms and processes of urban and agricultural nonpoint sources (NPSs) show clear differences, which lead to distinct characteristics of water quality responses with mixed land-uses catchments compared to single land-use ones. However, few studies have provided such insights in these characteristic or quantified different water environment responses to NPS pollution. In this study, an integrated modelling approach was developed for those complex catchments by combining three commonly used models: SWMM (Storm Water Management Model), SWAT (Soil and Water Assessment Tool) and MIKE 11. A case study was performed in a typical urban–rural catchment of Chao Lake, China. The simulated results indicated that urban NPS pollution responded sensitively to rainfall events and was greatly affected by the antecedent dry days. Compare to urban NPS, agricultural NPS pollution was characterized with the time-lag to rainfall depended on soil moisture and the post-rain-season emissions carried by lateral flows, and were also affected by the local farm-practice schedule. With comprehensive impacts from urban-rural land-uses, the time-interleaved urban and agricultural NPS pollution emissions and more abundant pollution accumulation both led to a decrease in the responsive time and an increase in the frequency of peak pollution concentration values even during the dry season. These obtained characteristics can provide guidance for drafting watershed management plans in similar mixed land use catchments.

Assessment of Air Quality Index for Cities and Major Towns in Tamil Nadu, India

Air Pollution may be defined as any atmospheric condition in which certain substances are present in such concentrations that they can produce undesirable effects on man and his environment. The Air Quality Index (AQI) is a reporting system and an important tool of risk communication. It informs the public about the level of ambient air quality, and the potential health risk it would impose. AQI converts complex air quality data of various pollutants into a single number (index value), nomenclature and colour. AQI is represented as numeric value varies from 0 to 500. If score is 0, it is the best air quality and if score is 500, it is the worst air quality. There are six AQI categories, namely Good, Satisfactory, Moderate, Poor, Very Poor, and Severe. Each of these categories is decided based on ambient concentration values of air pollutants and their likely health impacts. In Tamil Nadu, under National Ambient Air Quality Monitoring Programme, Ambient Air Quality (AAQ) is being monitored by Central Pollution Control Board in association with Tamil Nadu Pollution Control Board in 28 locations covering cities, major towns and major industrial areas viz. Chennai, Salem, Coimbatore, Madurai, Trichy, Cuddalore, Mettur, and Thoothukudi. The AAQ data from January 2015 to December 2015 for the above cities and towns is collected and AQI is calculated for four months covering four seasons (i.e.,) January (winter), May (summer), July (monsoon), November (post monsoon). Almost all the stations’ AQI fall under good and satisfactory category except Trichy where the majority of the days the AQI fall under moderate category. From the AQI of all the stations, it is observed that responsible pollutant is PM10. The other parameter (i.e.,) SO2 and NO2 fall under good category for all stations for all days. The higher value of PM10 is mainly due to vehicular pollution.

Multi-phase transformation model of water quality in the sluice-controlled river reaches of Shayinghe River in China.

To better understand the complex transformation mechanisms of pollutants in different phases in sluice-controlled river reaches (SCRRs), a multi-phase transformation model of water quality is proposed. This model mainly describes the interactions of the water body, suspended matter, deposited sediments, and organisms. Mathematical expressions were first derived to describe the mass transportation processes in different phases of the river system. The multi-phase transformation model in SCRRs was then established with defined physical mechanisms. Monitored data from the operation of Huaidian sluice were used to identify and validate the parameters of the transformation model and to simulate the spatial and temporal changes of pollutants in different phases. Four findings were made from the results. Firstly, the concentration values of pollutants in each phase in the upper and lower river reaches of the sluice are affected by flow, mode of sluice operation, and algal growth and enrichment. Secondly, the reaction processes in the upper and lower river reaches of the sluice indicate different dominant mechanisms according to the change in sluice operation. Thirdly, sluice operation leads to stronger exchanges between the water body and external materials because of the increased water disturbance. Fourthly, in the early period of the experiment, changes in the alga concentrations were mainly affected by water movement. In the later period, changes in the alga concentrations were mainly affected by the obstruction of the sluice in the upstream section, while these were affected by flow velocity, flow volume, and changes in nutrient concentration in the downstream section.

Quality control and gap-filling of PM10 daily mean concentrations with the best linear unbiased estimator.

According to the European Directive 2008/50/CE, the air quality assessment consists in the measurement of the concentration fields, and the evaluation of the mean, number of exceedances, etc. of some chemical species dangerous to human health. The measurements provided by an air quality ground-based monitoring network are the main information source but the availability of these data is often limited by several technical and operational problems. In this paper, the best linear unbiased estimator (BLUE) is proposed to validate the pollutant concentration values and to fill the gaps in the measurement of time series collected by a monitoring network. The BLUE algorithm is tested using the daily mean concentrations of particulate matter having aerodynamic diameter less than 10μ (PM10 concentrations) measured by the air quality monitoring sensors operating in the Lazio Region in Italy. The comparison between the estimated and measured data evidences an error comparable with the measurement uncertainty. Due to its simplicity and reliability, the BLUE will be used in the routine quality test procedures of the Lazio air quality monitoring network measurements.