Environmental Engineering
P. Saxena; S. Sonwani
Abstract
The indoor air quality is much more matter of concern as relative to ambient or outdoor air quality, especially in the context of human health. However, very few studies have been reported for remediation of indoor ozone by plant species. The main objective of this study is to evaluate ozone deposition ...
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The indoor air quality is much more matter of concern as relative to ambient or outdoor air quality, especially in the context of human health. However, very few studies have been reported for remediation of indoor ozone by plant species. The main objective of this study is to evaluate ozone deposition velocities and ozone removal effectiveness of three indoor ornamental plant species (Dracaena deremensis, Tagetes erecta and Lilium candidum) that can be used in the remediation of indoor ozone. Ozone deposition velocity was estimated through measurement of leaf surface areas of selected plant species and exposing them to 3-regular daytime cycles where ozone concentrations under controlled conditions first increased from 8 h followed by 16 h in the absence of ozone. Values of ozone deposition velocity after the completion of first exposure were found maximum (7.7 m/h) in case of Dracaena deremensis and minimum (0.5 m/h) after the completion third exposure in Lilium candidum. The ozone removal effectiveness found in the range of 0.7 to 13% for leaf surface area to room volume ratio of 0.06/m with reference to an air exchange system and background loss present in an indoor environment. Among the selected plant species, Dracaena deremensis has got the highest ozone deposition velocity as well as ozone removal effectiveness and Lilium candidum has got the lowest values. Hence, this study concludes with the sustainable use of ornamental plant species in the remediation of the indoor ozone pollution, which can further help in improving the health condition of the residents.
Environmental Management
M.H. Masum; S.K. Pal
Abstract
Air pollution has become a serious concern for its potential health hazard, however, often got less attention in developing countries, like Bangladesh. It is expected that worldwide lockdown due to COVID-19 widespread cause reduction in environmental pollution in particularly the air pollution: however, ...
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Air pollution has become a serious concern for its potential health hazard, however, often got less attention in developing countries, like Bangladesh. It is expected that worldwide lockdown due to COVID-19 widespread cause reduction in environmental pollution in particularly the air pollution: however, such changes have been different in different places. In Chittagong, a city scale lockdown came in force on 26 March 2020, a week after when first three cases of COVID-19 have been reported in Bangladesh. This study aims to statistically evaluate the effects of COVID-19 lockdown (26 March to 26 April 2020) on selected air quality pollutants and air quality index s. The daily average concentrations of air pollutants PM10, PM2.5, NO2, SO2 and CO of Chittagong city during COVID-19 lockdown were statistically evaluated and were compared with dry season data averaging over previous 8 years (2012 to 2019). During lockdown, except NO2, all other pollutants studied showed statistically significant decreasing trend. During the COVID-19 shutdown notable reduction of 40%, 32% and 13% compared to the daily mean concentrations of these previous dry season were seen for PM2.5, PM10 and NO2, respectively. The improvement in air quality index value was found as 26% in comparison to the previous dry season due to less human activities in COVID-19 shutdown. The factor analysis showed that AQI in Chittagong city is largely influenced by PM10 and PM2.5 during COVID-19 shutdown. The lesson learnt in this forced measure of lockdown is not surprising and unexpected. It is rather thought provoking for the decision makers to tradeoff the tangible air quality benefits with ongoing development strategies’ that was often overlooked directly or indirectly.
A. Masih
Abstract
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have ...
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Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affect the performance of an algorithm, however, it is yet to be known why an algorithm is preferred over the other for a certain task. The work aims at highlighting the underlying principles of machine learning techniques and about their role in enhancing the prediction performance. The study adopts, 38 most relevant studies in the field of environmental science and engineering which have applied machine learning techniques during last 6 years. The review conducted explores several aspects of the studies such as: 1) the role of input predictors to improve the prediction accuracy; 2) geographically where these studies were conducted; 3) the major techniques applied for pollutant concentration estimation or forecasting; and 4) whether these techniques were based on Linear Regression, Neural Network, Support Vector Machine or Ensemble learning algorithms. The results obtained suggest that, machine learning techniques are mainly conducted in continent Europe and America. Furthermore a factorial analysis named multi-component analysis performed show that pollution estimation is generally performed by using ensemble learning and linear regression based approaches, whereas, forecasting tasks tend to implement neural networks and support vector machines based algorithms.
I. Kayes; S.A. Shahriar; K. Hasan; M. Akhter; M.M. Kabir; M.A. Salam
Abstract
Meteorological parameters play a significant role in affecting ambient air quality of an urban environment. As Dhaka, the capital city of Bangladesh, is one of the air pollution hotspot among the megacities in the world, however the potential meteorological influences on criteria air pollutants for this ...
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Meteorological parameters play a significant role in affecting ambient air quality of an urban environment. As Dhaka, the capital city of Bangladesh, is one of the air pollution hotspot among the megacities in the world, however the potential meteorological influences on criteria air pollutants for this megacity are remained less studied. The objectives of this research were to examine the relationships between meteorological parameters such as daily mean temperature (o C), relative humidity (%) and rainfall (mm) and, the concentration of criteria air pollutants (SO2, CO, NOx, O3, PM2.5 and PM10) from January, 2013 to December, 2017. This study also focused on the trend analysis of the air pollutants concentration over the period. Spearman correlation was applied to illustrate the relationships between air pollutants concentration and temperature, relative humidity and rainfall. Multiple linear and non-linear regressions were compared to explore potential role of meteorological parameters on air pollutants' concentrations. Trend analysis resulted that concentration of SO2 is increasing in the air of Dhaka while others are decreasing. Most of the pollutants resulted negative correlation with atmospheric temperature and relative humidity, however, they showed variable response to seasonal variation of meteorological parameters. Regression analysis resulted that both the multiple non-linear and linear model performed similar for predicting concentrations of particulate matters but for gaseous pollutants both model performances were poor. This research is expected to contribute in improving the forecast accuracy of air pollution under variable meteorological parameters considering seasonal fluctuations.
A.K.R. Kouao; E.T. N’datchoh; V. Yoboue; S. Silue; H. Attoh; M. Coulibaly; T. Robins
Abstract
Indoor air pollution associated with cooking and heating biomass fuel burning is estimated to be responsible for 7 million deaths in 2016 and most of these deaths occur in low and middle income countries. In Côte d'Ivoire, 73% of the population is reported using biomass (charcoal or wood) for cooking. ...
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Indoor air pollution associated with cooking and heating biomass fuel burning is estimated to be responsible for 7 million deaths in 2016 and most of these deaths occur in low and middle income countries. In Côte d'Ivoire, 73% of the population is reported using biomass (charcoal or wood) for cooking. The active device 3M EVM-7 was used to measure PM2.5 daily average concentrations inside and outside households in areas close (Andokoi) and far (Lubafrique) to an industrial zone in two popular neighborhoods of Yopougon, the largest and most populated municipality of the city of Abidjan (Côte d’Ivoire). PM2.5 daily average concentrations indoors and outdoors are respectively 121±12 µg/m3 and 117±8 µg/m3 in Andokoi and 32±3 µg/m3 and 41±4 µg/m3 in Lubafrique well above the World Health Organization guideline value (25 µg/m3) for air quality. Using multivariable models, the results were the number of windows in bedrooms and kitchens located outdoor were negatively correlated with the concentration of indoor PM2.5. The outdoor concentrations of PM2.5, were higher according to the cooking fuel type.
A. Jaiswal; C. Samuel; V.M. Kadabgaon
Abstract
The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air ...
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The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air pollutant levels are predicted. Carbon monoxide, nitrogen dioxide, sulphur dioxide, particulate matter particles as PM2.5 and PM10 are the pollutants on which the study focuses. Mann-Kendall and Sen’s slope estimator tests are used on summer (February-May), monsoon (June-September) and winter (October-January) seasonal data from year 2013 to 2016 and trend results and power of the slopes are estimated. For predictive analysis, different autoregressive integrated moving average models are compared with goodness of fit statistics, and the observed results stated autoregressive integrated moving average (1,1,1) as the best-suited for forecast modeling of different pollutants in Varanasi. Autoregressive integrated moving average model (1,1,1) is also used on the annual concentration levels to predict forthcoming year's annual pollutants value. Study reveals that PM 10 shows a rising trend with predicted approximate annual concentration of 273 µg/m3 and PM2.5, carbon monoxide, nitrogen dioxide and sulphur dioxide show a reducing trend with approximate annual concentration of 139 µg/m3, 1.37 mg/ m3, 38 µg/m3 and 17 µg/m3, respectively, by the year 2030. The study predicted carbon monoxide, nitrogen dioxide andsulphur dioxide concentrations are lower and PM10 and PM2.5 concentrations are much higher to the standard permissible limits in future years also, and specific measures are required to control emissions of these pollutants in Varanasi.
D.S. Kumar; S.H. Bhushan; D.A. Kishore
Abstract
Dispersion modeling approach was applied for the determination of SO2 and NO2 pollution in the ambient air. The model performance has been evaluated by comparing the measured and predicted concentrations of SO2 and NO2. This has been tested to measure the air quality and predicted incremental value of ...
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Dispersion modeling approach was applied for the determination of SO2 and NO2 pollution in the ambient air. The model performance has been evaluated by comparing the measured and predicted concentrations of SO2 and NO2. This has been tested to measure the air quality and predicted incremental value of pollutant’s concentrations by using the data available from the industrial and mining cluster for a period of one year covering from March’ 2015 to February’ 2016 where more accuracy and specific result oriented is concerned. The maximum cumulative predicted value of SO2 is 6.99 µg/m3 and NO2 is 15.98 µg/m3. It has been found that the overall resultant concentrations are far below the prescribed standard in all stations. As revealed from the present research that, there is no such pollution impact to the nearby villages where industrial and mining activities are concerned in the study area. This paper can be used as better reference for further and future research in the area, as there is no such study has been carried out before in the specific area.
M. Memarianfard; A.M. Hatami; M. Memarianfard
Abstract
Most parts of the urban areas are faced with the problem of floating fine particulate matter. Therefore, it is crucial to estimate the amounts of fine particulate matter concentrations through the urban atmosphere. In this research, an artificial neural network technique was utilized to model the PM2.5 ...
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Most parts of the urban areas are faced with the problem of floating fine particulate matter. Therefore, it is crucial to estimate the amounts of fine particulate matter concentrations through the urban atmosphere. In this research, an artificial neural network technique was utilized to model the PM2.5 dispersion in Tehran City. Factors which are influencing the predicted value consist of weather-related and air pollution-related data, i.e. wind speed, humidity, temperature, SO2, CO, NO2, and PM2.5 as target values. These factors have been considered in 19 measuring stations (zones) over urban area across Tehran City during four years, from March 2011 to March 2015. The results indicate that the network with hidden layer including six neurons at training epoch 113, has the best performance with the lowest error value (MSE=0.049438) on considering PM2.5 concentrations across metropolitan areas in Tehran. Furthermore, the “R” value for regression analysis of training, validation, test, and all data are 0.65898, 0.6419, 0.54027, and 0.62331, respectively. This study also represents the artificial neural networks have satisfactory implemented for resolving complex patterns in the field of air pollution.
M.J. Mohammadizadeh; A.R. Karbassi; Gh.R. Nabi Bidhendi; M. Abbaspour
Abstract
The aim of this study is to evaluate the obstacles due to a DPSIR model combined with fuzzy analytic hierarchy process technique. Hence, to prioritize the responses regarding the driving forces, pressures, states and impacts, the hierarchy of the model is established. Evaluations and prioritization of ...
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The aim of this study is to evaluate the obstacles due to a DPSIR model combined with fuzzy analytic hierarchy process technique. Hence, to prioritize the responses regarding the driving forces, pressures, states and impacts, the hierarchy of the model is established. Evaluations and prioritization of model results of urban transport situation in Tehran have provided a number of necessary issues for strategic planning to reduce local air pollution and emission of greenhouse gases by prioritizing their effectiveness in the implementation, including; a) development and improvement of public transport (R1), b) improvement of fuel quality (R2), c) improvement of vehicle emission standards (R3), d) vehicle inspection (R4), f) traffic management (R5). In this study, responses to improve the factors of pressure, stimulus, the current state and the impacts were examined and compared hierarchically. Finally, their priority relative to each other was achieved. Development and improvement of public transport, improvement of the quality of fuel, improvement of vehicle emission standards, vehicle check-up and finally urban traffic management were identified respectively as practical steps to control and reduce air pollution in Tehran.
A. Zabihi; M.R. Raazaitabari
Abstract
The significant consumption of gas in the World results in the emission of greenhouse gases into atmosphere. Abadan refinery has always been the biggest and oldest oil refinery in the Middle East and has a variety of refined products. After six months of collecting data about pollutant concentration ...
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The significant consumption of gas in the World results in the emission of greenhouse gases into atmosphere. Abadan refinery has always been the biggest and oldest oil refinery in the Middle East and has a variety of refined products. After six months of collecting data about pollutant concentration emitted from a stack of old furnaces of units 75 and new unit 200, the emission factor of the pollutant was calculated. The result showed that the emission factor of some hazardous pollutants emitted from old unit 75 was tremendously higher than that of unit 200. This study suggests the installation of a forced fan to provide the excess air and a feed temperature controlling system to control fuel gas consumption. These would make the fuel combustion complete and decrease its consumption. Meanwhile, further results showed that the renovation of unit 75 could lead to a significant annual reduction of some pollutants such as CO, H2S, and CxHx to 66 ton, 3 ton, and 800 kg, respectively; this would increase the emission rate of pollutant SO2 up to 150 ton annually. Finally, the new data of pollution coming from unit 75 were compared to pollution standard at American refineries. Results showed that the emission factor of most pollutants were below the American standard limits. However, the emission factor of sulfur dioxide emitted from upgraded furnace of unit 75 surpassed the American standard values. Fuel gas needs to be free of hydrogen sulfide in order to decrease SO2 emission in unit 75. It is predicted that the renovation of other 11 old furnaces belonging to Abadan refinery will result in significant decrease of pollutants CO, CxHx and H2S up to 320, 94 and 76 ton annually.
L. Trizio; L. Angiuli; M. Menegotto; F. Fedele; R. Giua; F. Mazzone; A.G.C. Carducci; R. Bellotti; G. Assennato
Abstract
During the last years, several exceedances of PM10 and benzo(a)pyrene limit values exceedances were recorded in Taranto, a city in southern Italy included in so-called areas at high risk of environmental crisis because of the presence of a heavy industrial district including the largest steel factory ...
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During the last years, several exceedances of PM10 and benzo(a)pyrene limit values exceedances were recorded in Taranto, a city in southern Italy included in so-called areas at high risk of environmental crisis because of the presence of a heavy industrial district including the largest steel factory in Europe. A study of these critical pollution events showed a close correlation with the wind coming from the industrial site to the adjacent urban area. During 2011, at monitoring sites closes to the industrial area, at least the 65% of PM10 exceedances were related to wind day conditions (characterized by at least 3 consecutive hours of wind coming from 270-360±2deg with an associated speed higher than 7 m/s). For this reason, in 2012 an integrated environmental permit and a regional air quality plan were enacted to reduce pollutant emissions from industrial plants. A study of PM10 levels registered during windy days was performed during critical episodes of pollution highlighting that the difference between windy days and no windy days’ concentrations reduces from 2012 to 2014 in industrial site. False negative events (verified ex-post by observed meteorological data) not identified by the forecast model - did not show a significant influence on PM concentration: PM10 values were comparable and sometimes lower than windy days levels. It is reasonable that the new scenario with a relevant reduction emissions form Ilva plant reduced the pollutants contribution from industrial area, contributing to PM10 levels decrease, also in false negative events.