This document provides an analysis of air pollutant distributions from a typical construction site in Brazil. Six major air pollutants are analyzed using the Gaussian plume method. Based on the analysis and air quality standards from Brazil and WHO, emissions of nitrogen dioxide, carbon monoxide, and sulfur dioxide from the construction site pose the highest risk to surrounding areas within 100 meters. Mitigation measures are recommended to control emissions of these pollutants.
O presente trabalho tem por objetivo utilizar o Método dos Mínimos Quadrados (MMQ) para analisar através do coeficiente de determinação (R2), qual modelo que melhor se ajusta ao comportamento do conjunto de dados da concentração de HCFC-142b em partes por trilhão entre os anos de 1992 a 2018. Ademais, pretende-se fazer estimativas de concentrações futuras entre 5 e 10 períodos em cada um dos modelos de ajuste.
The document discusses the health impacts of particulate matter (PM2.5) air pollution. It notes that exposure to anthropogenic PM2.5 leads to reduced life expectancy, with models showing losses ranging from months to over a year depending on the year and meteorological factors. Motor vehicles are identified as major contributors to air pollution in cities, responsible for around half of particulate emissions. Long term exposure to elevated levels of PM2.5 and other air pollutants increases mortality rates from respiratory and cardiac causes.
Mapping urban air pollution using gis a regression based approachAnandhi Ramachandran
This document describes a regression-based methodology developed as part of the SAVIAH project to map traffic-related air pollution, specifically NO2, within a GIS environment in three cities. Monitoring of NO2 was conducted using passive diffusion tubes over four periods. A GIS was created containing pollution, road, traffic, land use and other data. Regression equations were constructed using 80 monitoring sites to predict pollution across study areas. Map accuracy was assessed by comparing predictions to independent monitoring sites, showing good prediction (r2 = 0.79-0.87). Further monitoring found maps provided reliable estimates the following year (r2 = 0.59-0.86).
This document discusses criteria for setting ambient air quality standards. It outlines several factors that influence decision making for standards, including acceptable health risks, control costs, and scientific judgment. Standards are designed to protect public health from air pollution effects and may establish maximum concentrations. Primary standards protect health, while secondary standards protect welfare. Factors like meteorology, geography, exposure levels, health risks, economics and policies must be considered when setting standards.
AIR POLLUTION CONTROL course material by Prof S S JAHAGIRDAR,NKOCET,SOLAPUR for BE (CIVIL ) students of Solapur university. Content will be also useful for SHIVAJI and PUNE university students
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENTAM Publications
Interest in air pollution investigation of urban environment due to existence of industrial and commercial activities along with vehicular emission and existence of buildings and streets which setup natural barrier for pollutant dispersion in the urban environment has increased. The air pollution modelling is a multidisciplinary subject when the entire cities are taken under consideration where urban planning and geometries are complex which needs a large software packages to be developed like Operational Street Pollution Model (OSPM), California Line Source model (CALINE series) etc. On overviewing various works it can be summarized that the air pollutant dispersion in urban street canyons and all linked phenomenon such as wind flow, pollutant concentrations, temperature distribution etc. generally depend on wind speed and direction, building heights and density, road width, source and intensity of air pollution, meteorological variables like temperature, humidity etc. A unique and surprising case is observed every time on numerous combinations of these factors. The main aim of this study is to simulate the atmospheric pollutant dispersion for given pollutant like carbon monoxide, sulphur dioxide and nitrogen dioxide and given atmospheric conditions like wind speed and direction. Computational Fluid Dynamics (CFD) simulation for analysing the atmospheric pollutant dispersion is done after natural airflow analysis. Volume rendering is done for variables such as phase 2 volume fraction and velocity with resolution as 250 pixels per inch and transparency as 20%. It can be observed that all the three pollutant namely nitrogen dioxide, sulphur dioxide and carbon monoxide the phase 2 volume fraction changes from 0 to 1. The wind velocity changes from 3.395×10-13 m/s to 1.692×102 m/s. The dispersion of pollutants follow the sequence Sulphur dioxide>Carbon monoxide>Nitrogen dioxide.
This study investigated spatial patterns of air pollution in an industrial estate in Lagos, Nigeria. Seven sampling sites were selected to measure levels of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), hydrogen sulfide (H2S), methane (CH4), and noise. Statistical analysis using principal component analysis and cluster analysis revealed two main sources of air pollution: traffic emissions contributed most to NO2 levels, while fossil fuel combustion and industrial sources contributed most to SO2, CO, and H2S levels. The levels of SO2, NO2, and PM10 exceeded national standards at some sites, indicating traffic and industrial pollution are problems. Appropriate vehicle emission controls
Introduction
The transport sectors, including land transport, shipping and
aviation, are major sources of atmospheric pollution (e.g.,
Righi et al., 2013). The emissions from transport are growing
more rapidly than those from the other anthropogenic activities.
According to the ATTICA assessment (Uherek et al.,
2010; Eyring et al., 2010), land transport and shipping shared
74 and 12 % of the global CO2 emissions from transport in
the year 2000, respectively. In the time period 1990–2007,
the EU-15 CO2-equivalent emissions from land transport and
shipping increased by 24 and 63 %, respectively. This growth
is expected to continue in the future, due to increasing world
population, economic activities and related mobility. The future
road traffic scenarios analyzed by Uherek et al. (2010)
essentially agree in projecting an increase of both fuel demand
and CO2 emissions until 2030, with up to a factor of
∼ 3 increase in CO2 emissions with respect to 2000. The ATTICA
assessment also showed that emissions of CO2 from
land transport and shipping affect the global climate by exerting
a radiative forcing (RF) effect of 171 (year 2000)
and 37 mW m−2
(year 2005), respectively. These two sectors
together account for 13 % of the total anthropogenic CO2
warming (year 2005).
In addition to long-lived greenhouse gases, ground-based
vehicles and ocean-going ships emit aerosol particles as well
as a wide range of short-lived gases, including also aerosol
precursor species. Atmospheric aerosol particles have significant
impacts on climate, through their interaction with solar
radiation and their ability to modify cloud microphysical
and optical properties (Forster et al., 2007). In populated areas,
they also affect air quality and human health (Pope and
Dockery, 2006; Chow et al., 2006).
O presente trabalho tem por objetivo utilizar o Método dos Mínimos Quadrados (MMQ) para analisar através do coeficiente de determinação (R2), qual modelo que melhor se ajusta ao comportamento do conjunto de dados da concentração de HCFC-142b em partes por trilhão entre os anos de 1992 a 2018. Ademais, pretende-se fazer estimativas de concentrações futuras entre 5 e 10 períodos em cada um dos modelos de ajuste.
The document discusses the health impacts of particulate matter (PM2.5) air pollution. It notes that exposure to anthropogenic PM2.5 leads to reduced life expectancy, with models showing losses ranging from months to over a year depending on the year and meteorological factors. Motor vehicles are identified as major contributors to air pollution in cities, responsible for around half of particulate emissions. Long term exposure to elevated levels of PM2.5 and other air pollutants increases mortality rates from respiratory and cardiac causes.
Mapping urban air pollution using gis a regression based approachAnandhi Ramachandran
This document describes a regression-based methodology developed as part of the SAVIAH project to map traffic-related air pollution, specifically NO2, within a GIS environment in three cities. Monitoring of NO2 was conducted using passive diffusion tubes over four periods. A GIS was created containing pollution, road, traffic, land use and other data. Regression equations were constructed using 80 monitoring sites to predict pollution across study areas. Map accuracy was assessed by comparing predictions to independent monitoring sites, showing good prediction (r2 = 0.79-0.87). Further monitoring found maps provided reliable estimates the following year (r2 = 0.59-0.86).
This document discusses criteria for setting ambient air quality standards. It outlines several factors that influence decision making for standards, including acceptable health risks, control costs, and scientific judgment. Standards are designed to protect public health from air pollution effects and may establish maximum concentrations. Primary standards protect health, while secondary standards protect welfare. Factors like meteorology, geography, exposure levels, health risks, economics and policies must be considered when setting standards.
AIR POLLUTION CONTROL course material by Prof S S JAHAGIRDAR,NKOCET,SOLAPUR for BE (CIVIL ) students of Solapur university. Content will be also useful for SHIVAJI and PUNE university students
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENTAM Publications
Interest in air pollution investigation of urban environment due to existence of industrial and commercial activities along with vehicular emission and existence of buildings and streets which setup natural barrier for pollutant dispersion in the urban environment has increased. The air pollution modelling is a multidisciplinary subject when the entire cities are taken under consideration where urban planning and geometries are complex which needs a large software packages to be developed like Operational Street Pollution Model (OSPM), California Line Source model (CALINE series) etc. On overviewing various works it can be summarized that the air pollutant dispersion in urban street canyons and all linked phenomenon such as wind flow, pollutant concentrations, temperature distribution etc. generally depend on wind speed and direction, building heights and density, road width, source and intensity of air pollution, meteorological variables like temperature, humidity etc. A unique and surprising case is observed every time on numerous combinations of these factors. The main aim of this study is to simulate the atmospheric pollutant dispersion for given pollutant like carbon monoxide, sulphur dioxide and nitrogen dioxide and given atmospheric conditions like wind speed and direction. Computational Fluid Dynamics (CFD) simulation for analysing the atmospheric pollutant dispersion is done after natural airflow analysis. Volume rendering is done for variables such as phase 2 volume fraction and velocity with resolution as 250 pixels per inch and transparency as 20%. It can be observed that all the three pollutant namely nitrogen dioxide, sulphur dioxide and carbon monoxide the phase 2 volume fraction changes from 0 to 1. The wind velocity changes from 3.395×10-13 m/s to 1.692×102 m/s. The dispersion of pollutants follow the sequence Sulphur dioxide>Carbon monoxide>Nitrogen dioxide.
This study investigated spatial patterns of air pollution in an industrial estate in Lagos, Nigeria. Seven sampling sites were selected to measure levels of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), hydrogen sulfide (H2S), methane (CH4), and noise. Statistical analysis using principal component analysis and cluster analysis revealed two main sources of air pollution: traffic emissions contributed most to NO2 levels, while fossil fuel combustion and industrial sources contributed most to SO2, CO, and H2S levels. The levels of SO2, NO2, and PM10 exceeded national standards at some sites, indicating traffic and industrial pollution are problems. Appropriate vehicle emission controls
Introduction
The transport sectors, including land transport, shipping and
aviation, are major sources of atmospheric pollution (e.g.,
Righi et al., 2013). The emissions from transport are growing
more rapidly than those from the other anthropogenic activities.
According to the ATTICA assessment (Uherek et al.,
2010; Eyring et al., 2010), land transport and shipping shared
74 and 12 % of the global CO2 emissions from transport in
the year 2000, respectively. In the time period 1990–2007,
the EU-15 CO2-equivalent emissions from land transport and
shipping increased by 24 and 63 %, respectively. This growth
is expected to continue in the future, due to increasing world
population, economic activities and related mobility. The future
road traffic scenarios analyzed by Uherek et al. (2010)
essentially agree in projecting an increase of both fuel demand
and CO2 emissions until 2030, with up to a factor of
∼ 3 increase in CO2 emissions with respect to 2000. The ATTICA
assessment also showed that emissions of CO2 from
land transport and shipping affect the global climate by exerting
a radiative forcing (RF) effect of 171 (year 2000)
and 37 mW m−2
(year 2005), respectively. These two sectors
together account for 13 % of the total anthropogenic CO2
warming (year 2005).
In addition to long-lived greenhouse gases, ground-based
vehicles and ocean-going ships emit aerosol particles as well
as a wide range of short-lived gases, including also aerosol
precursor species. Atmospheric aerosol particles have significant
impacts on climate, through their interaction with solar
radiation and their ability to modify cloud microphysical
and optical properties (Forster et al., 2007). In populated areas,
they also affect air quality and human health (Pope and
Dockery, 2006; Chow et al., 2006).
Reflections on diesel exposures in professional drivers - a poorly quantified...IES / IAQM
This document summarizes research on diesel exhaust exposures among professional drivers. It discusses several studies that have found high exposures and health effects among drivers such as taxi, truck, and bus drivers. The document outlines a new study called DEMiSt that aims to better characterize exposures for 200 drivers across sectors and identify strategies to reduce risks. Preliminary results suggest taxi drivers have the highest exposures while ventilation settings significantly impact levels. Reducing "pollution spikes" may substantially lower exposure. The study also examines exposures between electric and diesel taxis.
Study and analysis of the concentrations of tropospheric ozone in the city of...Enrique Posada
The document analyzes data from 9 air quality monitoring stations in Medellin, Colombia between 2014-2015. It finds:
- The average hourly ozone concentration was 15.7 ppb, with only 1.29% of readings exceeding the standard of 61 ppb.
- Concentrations were highest in southern stations and lowest in northern stations, peaking between 11am-3pm daily.
- Concentrations correlated with sunlight and were higher further from the river and in more urban areas.
- The situation was not considered serious from a public health perspective but deserves continued monitoring.
Nowadays by seeing the present scenario AIR is the essential element to live & Air Quality Index is a tool to distinguish the benefit of air quality. There are different methods to identify AQI, based on many impurities viz. PM2.5, PM10,CO were used to compare ambient air quality. By calculating AQI we define the quality level of air to be good, moderate, and hazardous as AQI is calculated by using the reference of "The United States Environmental Protection Agency" We are using thingspeak server to fetch the data into the cloud, so anyone can access the data in their respective location. We are not only focusing on stationary measurement but also on the real time value measurement of AQI. Which helps common people to access the Air Quality Index throughout the city and help them decide to stay in a cleaner air environment? Thus the foremost idea of AQI is to inform people about their air quality so they can step to defend their health.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Qualitative assessment of links between exposure to noise and air pollution a...IES / IAQM
The document summarizes research on the links between exposure to noise and air pollution, and socioeconomic status. Key findings include:
- Poorer groups often live and work in more polluted areas, and may be more susceptible to health impacts of pollution.
- Road traffic is a major source of both noise and air pollution in urban areas, where exposure is highest. Agriculture is a main source of air pollution.
- Research shows lower socioeconomic groups experience higher mortality and morbidity rates associated with air pollution exposure compared to higher socioeconomic groups.
- Children, the elderly, and those with pre-existing health conditions - who may be over-represented in lower socioeconomic groups - are more susceptible to health impacts of noise
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Quantification of rate of air pollution by means ofIJARBEST JOURNAL
To develop efficient strategies for pollution control, it is essential to assess
both the costs of control and the benefits that may result. These benefits will often include
improvements in public health, including reductions in both morbidity and premature
mortality. Until recently, there has been little guidance about how to calculate the benefits
of air pollution controls and how to use those estimates to assign priorities to different air
pollution control strategies. In this work, a method is described for quantifying the benefits
of reduced ambient concentrations of pollutants (such as ozone and particulate matter)
typically found in urban areas worldwide. The method applies the data on Jakara, Indonesia,
an area characterized by little wind, high population density (8 million people), congested
roads, and ambient air pollution. The magnitude of the benefits of pollution control depends
on the level of air pollution, the expected effects on health of the pollutants (dose-response),
the size of the population affected, and the economic value of these effects. In the case of
Jakarta, the methodology suggests that reducing exposure to lead and nitrogen dioxide
should also be a high priority. An important consequence of ambient lead pollution is a
reduction in learning abilities for children, measured as I.Q. loss. Apart from that, reducing
the proportion of respirable particles can reduce the amount of illness and premature
mortality.
This study examines the impact of oil and gas activity on non-methane volatile organic compound (NMVOC) emissions in the troposphere of the United States. It uses satellite data from the Ozone Monitoring Instrument to measure formaldehyde (HCHO) levels as a proxy for NMVOCs and nitrogen dioxide (NO2) levels as a proxy for oil and gas activity. Correlation analysis is conducted between HCHO and NO2 levels over various oil and gas production regions from 2005-2014. The Bakken oil field in North Dakota is identified as the best region for analysis due to minimal urban interference and topography. While NO2 levels in the Bakken field show a clear seasonal cycle correlated with oil production, HCHO
Urban Air Quality Modelling and Simulation: A Case Study of Kolhapur (M.S.), ...IDES Editor
As a consequence of urbanization a phenomenal
surge has been observed in the vehicular population in India,
giving rise to elevated levels of traffic related pollutants like
carbon monoxide, nitrogen oxides, hydrocarbons, and
particulates in Indian urban centers. These pollutants can
have both acute and chronic effects on human health. Thus
air quality management needs immediate attention. Air
quality models simulate the physical and chemical processes
occurring in the atmosphere to estimate the atmospheric
pollutant concentration. A variety of air quality models are
available ranging from simple empirical models to complex
Computational Fluid Dynamic (CFD) models. Air quality
models can be a valuable tool in pollution forecasting, air
quality management, traffic management and urban planning.
This paper evaluates the performance of widely used Danish
Operational Street Pollution Model (OSPM) under Indian
traffic conditions. Comparison between predicted and observed
concentrations was performed using both quantitative and
statistical methods. OSPM was found to perform exceedingly
well for the prediction of particulates whereas NO2 predictions
were poorly predicted.
Towards an accurate Ground-Level Ozone PredictionIJECEIAES
This paper motivation is to find the most accurate technique to predict the ground level ozone at Al Jahra station, Kuwait. The data on the meteorological variables (air temperature, relative humidity, solar radiation, direction and speed of wind) and concentration of seven pollutants of environment (SO2, NO2, NO, CO2, CO, NMHC, and CH4) were applied to forecast the ozone concentration in atmosphere. In this report, three methods (PLS regression, support vector machine (SVM), and multiple least-square regression) were used to predict ground-level ozone. We used Fifteen parameters to evaluate the performance of methods. Multiple least-square regression, partial least square regression (PLS regression), and SVM using linear and radial kernels were the best performers with MAE (mean absolute error) of 9.17x 10-03, 9.72 x 10-03, 9.64 x 10-03, and 9.12 x 10-03, respectively. SVM with polynomial kernel had MAE of 5.46 x 10-02. These results show that these methods could be used to predict ground-level ozone concentrations at Al Jahra station in Kuwait.
The document summarizes a summer internship that Juliann Chen completed at the Health Effects Institute in Boston. The Health Effects Institute is an independent non-profit organization that funds research on health effects of air pollution, especially from vehicles. During the internship, Chen helped review studies on health effects of air pollution in Asian cities and identified gaps where more research is needed. The internship provided experience in conducting literature reviews and epidemiology studies to understand impacts of air pollution.
The document discusses proposed revisions to ambient air quality criteria and standards in India. It reviews the health effects and dose-response relationships of several key air pollutants including benzene, carbon monoxide, formaldehyde, polycyclic aromatic hydrocarbons, arsenic, lead, mercury, nickel, vanadium, and oxides of nitrogen. For each pollutant, it discusses current levels in India, existing standards, rationale for proposed new standards based on health risks, and comparisons with standards in other countries. The approach focuses on establishing standards to protect human health based on toxicological data and risk assessments.
Fine Particulates In The Ambient Air Of Shillong ECRD IN
Fine particulate matter (PM2.5) was studied in Shillong, India using optical microscopy. The findings show:
1) The relative abundance of PM2.5 increases by 2.5% for every 1000 vehicles per hour from a background level of 59% due to traffic emissions.
2) Particles from traffic emissions enter the air mostly around 1 micron or less in size.
3) The size distribution of particles is similar within 13.6 m of ground level but higher altitudes have relatively more fine particles, possibly from gas condensation.
Preliminary Outdoor Air Pollution StudyNitin Yadav
This document outlines a study on outdoor air pollution in Noida City, India. The objectives are to determine the ambient air quality status, ascertain if air quality standards are violated, and develop preventive and corrective measures. Three monitoring stations were established to sample particulate matter (PM), sulfur dioxide (SO2), and nitrogen dioxide (NO2) using standard gravimetric and spectrophotometric methods. The study will involve sample collection, chemical analysis, comparing results to national standards, and providing recommendations based on outcomes and a literature review discussing previous air pollution studies. Expected results are ambient air quality data that can be compared to national standards to assess air quality in Noida City.
Righi et al_climate_impact_of_biofuels_in_shipping-global_model_studies_og_th...www.thiiink.com
ABSTRACT: Aerosol emissions from international shipping
are recognized to have a large impact on the Earth’s radiation
budget, directly by scattering and absorbing solar radiation and
indirectly by altering cloud properties. New regulations have
recently been approved by the International Maritime Organi-
zation (IMO) aiming at progressive reductions of the maximum
sulfur content allowed in marine fuels from current 4.5% by
mass down to 0.5% in 2020, with more restrictive limits already
applied in some coastal regions. In this context, we use a global
bottom-up algorithm to calculate geographically resolved emis-
sion inventories of gaseous (NOx, CO, SO2) and aerosol (black
carbon, organic matter, sulfate) species for different kinds of
low-sulfur fuels in shipping. We apply these inventories to study the resulting changes in radiative forcing, attributed to particles from shipping, with the global aerosol-climate model EMAC-MADE. The emission factors for the different fuels are based on measurements at a test bed of a large diesel engine. We consider both fossil fuel (marine gas oil) and biofuels (palm and soy bean oil) as a substitute for heavy fuel oil in the current (2006) fleet and compare their climate impact to that resulting from heavy fuel oil use. Our simulations suggest that ship-induced surface level concentrations of sulfate aerosol are strongly reduced, up to about 40-60% in the high-traffic regions. This clearly has positive consequences for pollution reduction in the vicinity of major harbors. Additionally, such reductions in the aerosol loading lead to a decrease of a factor of 3-4 in the indirect global aerosol effect induced by emissions from international shipping.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Dangerous gas explosion accidents result in considerable amount of casualties and property damage.
Hence, an investigation on the generation of poisonous gases in gas explosions exerts important implications
for accident prevention and control and in the decision-making processes of fire rescue. Therefore, a gas
explosion piping test system is established in this paper. Experimental research on gas explosion is conducted by
selecting methane/air premixed gases with concentrations of 7%, 9%, 11%, 13%, and 15% in the gas explosive
range. This research aims to reveal the regularity of CO generation after gas explosion in pipelines.
Experimental results showed that when the gas concentration is small (< 9%), 1500–3000 ppm CO will be
produced. When the gas concentration is large (> 9%), the CO amount will reach 3000–40000 ppm. The
variation trend in CO concentration and the quantity of explosive gas are also obtained.
Impact of Air Quality on Human Health In The Vicinity of Construction Sites i...IJERA Editor
Construction sites are important source of air pollution emitting pollutants like PM10, etc. which adversely affect human health especially the respiratory system. The present study aims at monitoring of PM10, health condition of workers, evaluation of API (Air Pollution Index) and development of correlation between API and human health in the vicinity of construction sites. In the present study relevant literature review has also been carried out to study and analyze the impact of air pollution on human health. Reconnaissance survey of 19 selected construction sites in Delhi-NCR has been conducted for the period January 2013 to December 2013 and health related data of people in the vicinity of construction sites has been collected individually through a questionnaire. The air quality data (for pollutant PM10) for the area in which the selected construction sites lie has been obtained from the continuous monitoring stations of Central Pollution Control Board. The monthly average PM10 concentration in the ambient air for the study period has been obtained for all the sites. The annual average PM10 level of all the sites has been estimated and compared with the prescribed value. Also the air pollution index (API) (for pollutant PM10) has been calculated for each site and compared with the percentage of people suffering with respiratory problems at the respective sites. The results show that the construction sites where the value of API for PM10 is higher there the percentage of people suffering with respiratory diseases has also been higher.
Air Pollution Prediction via Differential Evolution Strategies with Random Fo...IRJET Journal
This document discusses using a hybrid machine learning technique combining differential evolution and random forest methods to predict air pollution levels. It analyzes data on various pollutants from two cities in India - Delhi and Patna. The proposed approach is experimentally validated to achieve better performance compared to independent classifiers and multi-label classifiers in terms of accuracy, area under the curve, success index and correlation. Differential evolution is used to initialize population and optimize candidate solutions. Random forest creates an ensemble of decision trees to make predictions. The hybrid method is tested on predicting carbon monoxide, nitrogen dioxide and benzene levels using data from a monitoring station in Delhi.
An environmental impact assessment was conducted for a proposed integrated steel plant in Odisha, India. The summary finds:
1) Ambient air quality monitoring found existing PM10 and PM2.5 levels above national standards in the project area. Dispersion modeling also predicted the plant would significantly increase air pollution.
2) The EIA report underestimated health impacts by missing secondary particulate formation and incremental PM2.5 impacts. It also did not account for mercury or heavy metal emissions.
3) Based on estimated annual emissions of 9433 tons of PM, 13,131 tons of NOx, and 11,642 tons of SO2, a health impact assessment was conducted and found significant impacts from increased
Reflections on diesel exposures in professional drivers - a poorly quantified...IES / IAQM
This document summarizes research on diesel exhaust exposures among professional drivers. It discusses several studies that have found high exposures and health effects among drivers such as taxi, truck, and bus drivers. The document outlines a new study called DEMiSt that aims to better characterize exposures for 200 drivers across sectors and identify strategies to reduce risks. Preliminary results suggest taxi drivers have the highest exposures while ventilation settings significantly impact levels. Reducing "pollution spikes" may substantially lower exposure. The study also examines exposures between electric and diesel taxis.
Study and analysis of the concentrations of tropospheric ozone in the city of...Enrique Posada
The document analyzes data from 9 air quality monitoring stations in Medellin, Colombia between 2014-2015. It finds:
- The average hourly ozone concentration was 15.7 ppb, with only 1.29% of readings exceeding the standard of 61 ppb.
- Concentrations were highest in southern stations and lowest in northern stations, peaking between 11am-3pm daily.
- Concentrations correlated with sunlight and were higher further from the river and in more urban areas.
- The situation was not considered serious from a public health perspective but deserves continued monitoring.
Nowadays by seeing the present scenario AIR is the essential element to live & Air Quality Index is a tool to distinguish the benefit of air quality. There are different methods to identify AQI, based on many impurities viz. PM2.5, PM10,CO were used to compare ambient air quality. By calculating AQI we define the quality level of air to be good, moderate, and hazardous as AQI is calculated by using the reference of "The United States Environmental Protection Agency" We are using thingspeak server to fetch the data into the cloud, so anyone can access the data in their respective location. We are not only focusing on stationary measurement but also on the real time value measurement of AQI. Which helps common people to access the Air Quality Index throughout the city and help them decide to stay in a cleaner air environment? Thus the foremost idea of AQI is to inform people about their air quality so they can step to defend their health.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Qualitative assessment of links between exposure to noise and air pollution a...IES / IAQM
The document summarizes research on the links between exposure to noise and air pollution, and socioeconomic status. Key findings include:
- Poorer groups often live and work in more polluted areas, and may be more susceptible to health impacts of pollution.
- Road traffic is a major source of both noise and air pollution in urban areas, where exposure is highest. Agriculture is a main source of air pollution.
- Research shows lower socioeconomic groups experience higher mortality and morbidity rates associated with air pollution exposure compared to higher socioeconomic groups.
- Children, the elderly, and those with pre-existing health conditions - who may be over-represented in lower socioeconomic groups - are more susceptible to health impacts of noise
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Quantification of rate of air pollution by means ofIJARBEST JOURNAL
To develop efficient strategies for pollution control, it is essential to assess
both the costs of control and the benefits that may result. These benefits will often include
improvements in public health, including reductions in both morbidity and premature
mortality. Until recently, there has been little guidance about how to calculate the benefits
of air pollution controls and how to use those estimates to assign priorities to different air
pollution control strategies. In this work, a method is described for quantifying the benefits
of reduced ambient concentrations of pollutants (such as ozone and particulate matter)
typically found in urban areas worldwide. The method applies the data on Jakara, Indonesia,
an area characterized by little wind, high population density (8 million people), congested
roads, and ambient air pollution. The magnitude of the benefits of pollution control depends
on the level of air pollution, the expected effects on health of the pollutants (dose-response),
the size of the population affected, and the economic value of these effects. In the case of
Jakarta, the methodology suggests that reducing exposure to lead and nitrogen dioxide
should also be a high priority. An important consequence of ambient lead pollution is a
reduction in learning abilities for children, measured as I.Q. loss. Apart from that, reducing
the proportion of respirable particles can reduce the amount of illness and premature
mortality.
This study examines the impact of oil and gas activity on non-methane volatile organic compound (NMVOC) emissions in the troposphere of the United States. It uses satellite data from the Ozone Monitoring Instrument to measure formaldehyde (HCHO) levels as a proxy for NMVOCs and nitrogen dioxide (NO2) levels as a proxy for oil and gas activity. Correlation analysis is conducted between HCHO and NO2 levels over various oil and gas production regions from 2005-2014. The Bakken oil field in North Dakota is identified as the best region for analysis due to minimal urban interference and topography. While NO2 levels in the Bakken field show a clear seasonal cycle correlated with oil production, HCHO
Urban Air Quality Modelling and Simulation: A Case Study of Kolhapur (M.S.), ...IDES Editor
As a consequence of urbanization a phenomenal
surge has been observed in the vehicular population in India,
giving rise to elevated levels of traffic related pollutants like
carbon monoxide, nitrogen oxides, hydrocarbons, and
particulates in Indian urban centers. These pollutants can
have both acute and chronic effects on human health. Thus
air quality management needs immediate attention. Air
quality models simulate the physical and chemical processes
occurring in the atmosphere to estimate the atmospheric
pollutant concentration. A variety of air quality models are
available ranging from simple empirical models to complex
Computational Fluid Dynamic (CFD) models. Air quality
models can be a valuable tool in pollution forecasting, air
quality management, traffic management and urban planning.
This paper evaluates the performance of widely used Danish
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1. 1
1.0 INTRODUCTION
With the booming of the economy, dramatic developments occur in most countries
around the world, especially in some developing countries such as Brazil. The typical
phenomenon is the expansions of cities, which leads to increasing number of construction
projects to be operated each year. These projects represent the fast developments but also cause
environmental impacts, especially in the atmospheric quality within urban areas.
1.1 Executive Summary
The purpose of this project is to determine the distributions of major air pollutants
released from a typical construction site via studying a project located in Brazil and understand
the atmospheric impacts of the fast urban expansion. Two main studies are used as the references,
which include an essay Identification and Characterization of Particulate Matter Concentration
at Construction Jobsites by Araujo, Costa, and Moraes (2014) and a report Building Assemblies:
Construction Energy & Emissions conducted by University of British Columbia (1993).
Overall, six major atmospheric pollutants, namely Total Suspended Particulate (TSP),
PM10, PM2.5, Carbon Monoxide, Nitrogen Dioxide and Sulfur Dioxide, are analyzed in this
report. The method used in the analysis is the Gaussian Plume, the emission place is assumed to
be a point source at the center of the construction site, and the distribution of each pollutant is
summarized in a Table and a corresponding diagram. As can be seen in the session of analysis,
based on the standards for air pollutants conducted by Brazilian relevant authorities and World
Health Organization (WHO), the emissions of nitrogen dioxide, carbon monoxide and sulfur
dioxide are of highest significance, but for other 3 pollutants, their influences that should be
concerned do not exceed 100 meters along the centerlines of corresponding Gaussian Plumes
from the source.
2. 2
In order to understand the impacts on human health, Air Quality Health Index conducted
by Canadian environmental authority is also utilized in the analysis. According to the analysis
results, the risky zone is within the area 100 meters from the emission source.
After the analysis, the mitigating measures are provided. In terms of the mitigations,
controlling the emissions of carbon monoxide, nitrogen dioxide and sulfur dioxide is of
importance since their significant influences. Specifically, since the limitation of resources, the
efficiencies of controlling methods are assumed.
1.2 Problem Statement
As explained in the Session 1.0, with the dramatic growth of the global economy, an
increasing number of construction projects are introduced each year, which leads to the
environmental problems. In the urban area, the emissions of gas pollutants and particulate
matters is of significance; especially for the developing countries, this issue is much more drastic.
Air pollution can cause both acute and chronic effects on public health, impacting on a
large number of different systems and organs. These impacts range from minor upper respiratory
irritation to chronic heart and respiratory disease, lung cancer, acute respiratory infections in
children and chronic bronchitis in adults, aggravating pre-existing heart and lung disease, or
asthmatic attacks. Additionally, short and long-term exposures have also been linked with
reduced life expectancy and premature mortality [1].
3. 3
1.3 Methodology
This session introduces two methods used in the analysis, namely Gaussian Plume
Method and Air Quality Health Index Method.
1.3.1 Gaussian Plume Method
As introduced above, the gases in analysis can be divided into two categories, namely gas
pollutants and particulate matters. First, the particulate matters include PM 2.5, PM 10, and total
suspended particulates (TSP). The relevant data are obtained from an essay Identification and
Characterization of Particulate Matter Concentration at Construction Jobsites (Araujo, Costa,
and Moraes, 2014). Second, in terms of the gas pollutants, namely sulphur dioxide, carbon
monoxide, and nitrogen dioxide, the emission rates are obtained from a report Building
Assemblies: Construction Energy & Emissions conducted by University of British Columbia in
1993.
In terms of the analysis method, Gaussian Plume is used to establish the diffusion model.
In the Gaussian Model, it assumes that the air pollutants dispersion has a Gaussian distribution,
which is a normal probability distribution [2]. At present, Gaussian models are usually used to
predict the dispersion of non-continuous air pollution plumes. The basic Gaussian Plume
equation is concluded as:
𝑐 =
𝑄
2𝜋𝜎! 𝜎! 𝑢
𝑒
!
!!
!!!
!!
!!! !
!!!
!
Where:
𝐾! = 0.5𝜎!
!
𝑢
𝑥
𝐾! = 0.5𝜎!
!
𝑢
𝑥
𝑡 =
𝑥
𝑢
𝜎!, 𝜎! = ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 𝑎𝑛𝑑 𝑣𝑒𝑟𝑡𝑖𝑐𝑎𝑙 𝑑𝑖𝑠𝑝𝑒𝑟𝑠𝑖𝑜𝑛 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑠 (𝑚𝑒𝑡𝑒𝑟)
4. 4
Horizontal dispersion coefficient 𝜎! can be determined by the diagram shown in Figure 1,
and Figure 2 demonstrates the relationship between the downwind distance and vertical
dispersion coefficient 𝜎!. [3]
Specifically, since the construction site is close to the residential district with low-rise
buildings, only the air near the ground will be analyzed, which means “z-H” in the formula
should be 0.
Figure 1: Horizontal Dispersion Coefficient 𝝈 𝒚 as a Function of Downwind Distance from
the Source for Various Stability Categories.
5. 5
Figure 2: Vertical Dispersion Coefficient 𝝈 𝒛as a Function of Downwind Distance from the
Source for Various Stability Categories
In terms of the stability categories, Table 1 introduces the relevant information.
Table 1: Key to Stability Categories [4]
6. 6
1.3.2 Air Quality Health Index
The Air Quality Health Index (AQHI) is a public information tool used in Canada to
prevent people’s health from the negative effects of air pollution on a daily basis. Basically, it is
measured by the formula:
𝐴𝑄𝐻𝐼 =
100
10.4
× 𝑒!.!!!"#$×!!! − 1 + 𝑒!.!!!"#$×!!"! − 1 + 𝑒!.!!!"#$×!!"!.! − 1
Where:
𝑐!!
= Concentration of ozone, ppb
𝑐!"!
= Concentration of NO2, ppb
𝑐!"!.! = Concentration of PM2.5, 𝜇𝑔 𝑚!
After obtaining the value of Air Quality Health Index, the influence level can be
determined by Table 2.
Table 2: Canadian Air Quality Health Index Reference Table [5]
Specifically, in this report, only the points along the centerline of the Gaussian Plume are
analyzed by AQHI because the values along the centerline should be the most critical.
7. 7
1.4 Construction Site Introduction
The studied construction site is located in Salvador, Bahia, Brazil, (latitude 12°57’46”
south, longitude 38°24’32” west) at an altitude of 34 m. The proposed project contains 8
residential towers, each with 16 floors, totaling 464 housing units. The total construction area is
32,780 m2
. [6] Figure 3 introduces the construction location obtained by Google Earth.
Specifically, since there is no explanation of the dimensions of construction site, the area is
assumed to be a square, and the length of each side is 181 m.
The construction site is located in a low-rise residential district with the appearance of
flora and fauna, including a lake. Within an area of 100 m, there is no primary pollution source
such as other construction sites, industries, major highways and airports. [7]
Figure 3: Construction Site (via Google Earth)
A typical series of construction activities for reinforced concrete structures can be
categorized into 3 major phases, namely the earthwork, superstructure, and finishing. First, the
earthwork includes manual excavation, meso structure, razing of auger piles foundation,
vehicular traffic on the soil, land transportation, and truck traffic at the construction site. Second,
8. 8
in terms of the superstructure, it refers to the activities namely execution of reinforced concrete
components; lift masonry, mortar execution, and masonry shaft. Third, the finishing contains
finishing the external and internal mortar, grouting masonry façade, ceramic coating, crystallized
waterproofing, countertops marble/granite, lining plasterboard plates, and sanding.
These activities can produce significant amounts of air pollutants and impact the
atmospheric qualities.
9. 9
2.0 CASE STUDY
This session introduces the air quality standards posed by Brazilian relevant authorities
and World Health Organization, the means of data collections, and the analysis of atmospheric
pollutants by Gaussian Plume Method.
2.1 Air Quality Standards
In Brazil, the standardized pollutants are TSP, smoke, sulfur dioxide (SO2), inhalable
particles, carbon monoxide (CO), and nitrogen dioxide (NO2). The Brazilian National
Environmental Council (CONAMA) Resolution Number 3 published on August 1990 indicates
that the primary standards should be adopted if relevant area classes are not established [8].
Table 3: Brazilian National Air Quality Standards [9]
Pollutant Averaging Time Primary Standards Secondary Standards
TSP 24 h 240 𝜇𝑔 𝑚!
150 𝜇𝑔 𝑚!
Geometric Annual Average 80 𝜇𝑔 𝑚!
60 𝜇𝑔 𝑚!
PM10 24 h 150 𝜇𝑔 𝑚!
Arithmetic Annual Average 50 𝜇𝑔 𝑚!
SO2 24 h 365 𝜇𝑔 𝑚!
100 𝜇𝑔 𝑚!
Arithmetic Annual Average 80 𝜇𝑔 𝑚!
40 𝜇𝑔 𝑚!
CO 1 h 40,000 𝜇𝑔 𝑚!
8 h 10,000 𝜇𝑔 𝑚!
NO2 1 h 320 𝜇𝑔 𝑚!
190 𝜇𝑔 𝑚!
Arithmetic Annual Average 100 𝜇𝑔 𝑚!
Since there is no standard of PM2.5 in Brazil, the standard posted by World Health
Organization (WHO) will be used in this report. Thus, the PM2.5 concentration should be 25
µμg m!
within 24 hours, and 10 µμg m!
within a year.
10. 10
2.2 Data
This session explains the means of data collections, the specific numbers will be used for
further analysis in Session 2.3, and the situation of the meteorology.
2.2.1 Particulate matters
As introduced previously, the particulate matters include PM 2.5, PM 10, and Total
Suspended Particulates (TSP). According to the essay Identification and Characterization of
Particulate Matter Concentration at Construction Jobsites, the concentrations of these three
pollutants were obtained by the MiniVols Equipment because of it portability (the appearance is
shown in Figure 4). It was installed during three major construction phases, namely earthworks,
superstructure, and finishing; for each phase, the detection period was 10 days.
Figure 4: MiniVols [10]
In order to decrease the influence from the existed particulate matters in the air, there
were two sets of equipment were installed. One set was placed at the construction site entrance
for measuring the concentrations of PMs entering the construction site, and the other set was
installed at the end of the construction site for measuring the PMs exiting the construction site.
The measuring operations were performed at the same periods. The measuring period is
introduced in Table 4.
11. 11
Table 4: Measuring Schedule [11]
Shifts Schedule Period Length
Day 7 am to 3 pm 8 hours
Night 5 pm to 3 pm 22 hours
After adjusting the measurements by subtracting the PMs existing the air, the PMs
produced during the construction are shown in Table 5 [12].
Table 5: Descriptive Statistics of PM Concentrations in 𝝁𝒈 𝒎 𝟑
for Three Construction
Phases
As can be seen in Table 5, all the maximum average concentrations of three studied
particulate matters occurred during the Phase 2 that is superstructure. Results are 483.12 µμg m!
for TSP, 213.94 µμg m!
for PM10, and 77.85 µμg m!
for PM2.5. These results will be discussed
in later sessions.
2.2.2 Gas Pollutants
In terms of the gas pollutants, according to the report Building Assemblies: Construction
Energy & Emissions conducted by University of British Columbia in 1993, the maximum
emissions of three major gas pollutants are introduced in Table 6.
Table 6: Maximum Emissions of Three Major Gas pollutants [13]
Gas pollutants Maximum Emissions
CO 402.21 g/s
NOx 83.32 g/s
SO2 17.75 g/s
12. 12
In the further study, the concentration of nitrogen gas pollutants (NOx) is regarded as the
nitrogen dioxide (NO2).
2.2.3 Meteorology
During measuring the concentrations of particulate matters, the meteorological data were
also recorded and concluded in the essay Identification and Characterization of Particulate
Matter Concentration at Construction Jobsites (Araujo, Costa, and Moraes, 2014). Table 7
shows the relevant data.
Table 7: Meteorological Data during Measuring [14]
13. 13
According to Table 7, the average wind speed is 1.43 m/s, and the wind direction is
assumed to be the same in the further analysis. In addition, as can be seen in the column of
Pluviometry, most data are 0.0, which indicates that the incoming solar radiation should be
considered as “strong” in the analysis.
According to Table 1, during the daytime, the stability category should be A. No analysis
regarding of the night time will be posed. Based on Figure 1 and Figure 2, Gaussian Plume
Models can be formed for each air pollutants.
2.3 Analysis by Gaussian Plume Method
This session explains the detailed process of using Gaussian Plume to determine the
distributions of six air pollutants. In addition, relevant discussions for each pollutant are also
provided.
2.3.1 TSP
As can be seen in Session 2.2.1, the measurement of the TSP concentration is
483.12µμg m!
. In order to use the formula 𝑐 =
!
!!!!!!!
𝑒
!
!!
!!!
!!
!!! !
!!!
!
, it is assumed that the
measurement was processed at the point in the centerline of Gaussian Plume with a distance of
100m from the source; in addition, the pollution source is assumed as a point source. Specifically,
these assumptions are also applied to the analysis for other atmospheric pollutants.
First, since the distance is 100m (0.1 km), according to Figure 1and 2, 𝜎! and 𝜎! are
determined as 30m and 15m, and the point is in the centerline of the Gaussian Plume:
𝑐 =
𝑄
2𝜋𝜎! 𝜎! 𝑢
483.12 µμg m!
=
𝑄
2𝜋 30𝑚 15𝑚 (1.43 𝑚/𝑠)
14. 14
∴ 𝑄 = 1,953,365 µμg s
Secondly, find the values of 𝜎! and 𝜎! for different distances. In the analysis, 12
distances are chosen, namely 0.1 km, 0.2 km, 0.3 km, 0.4 km, 0.5 km, 0.6 km, 0.7 km, 0.8 km,
0.9 km, 1 km, 2 km, and 3 km. These distances are also discussed for other pollutants. Table 8
concludes the 𝜎! and 𝜎! values.
Table 8: 𝝈 𝒚 and 𝝈 𝒛 Values for 12 Chosen Distances
Distance
(km)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 2 3
𝝈 𝒚 30 50 65 80 100 120 150 170 200 205 400 500
𝝈 𝒛 15 30 40 60 80 140 190 280 390 470 1800 4000
Thirdly, determine the distribution of TSP in different distance. For each distance, except
for the point in centerline, 10 points in different ditances from the centerline, namely -50m, -40m,
-30, -20m, -10m, 10m, 20m, 30m, 40m, and 50m, are analyzed. Table 9 summarized the results.
Table 9: TSP Distribution Summary
TSP Distributions,
𝛍𝐠 𝐦 𝟑
Distance, km
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 2 3
DistancefromtheCenterline,m
-50 120 88 62 37 24 12 7 4 3 2 0 0
-40 199 105 69 40 25 12 7 4 3 2 0 0
-30 293 121 75 42 26 13 7 4 3 2 0 0
-20 387 134 80 44 27 13 8 5 3 2 0 0
-10 457 142 83 45 27 13 8 5 3 2 0 0
0 483 145 84 45 27 13 8 5 3 2 0 0
10 457 142 83 45 27 13 8 5 3 2 0 0
20 387 134 80 44 27 13 8 5 3 2 0 0
30 293 121 75 42 26 13 7 4 3 2 0 0
40 199 105 69 40 25 12 7 4 3 2 0 0
50 120 88 62 37 24 12 7 4 3 2 0 0
15. 15
According to the Brazilian national standard for Total Suspended Particulate (TSP)
indicated in Table 3, within 24 hours, the primary standard is 240 µμg m!
, and the secondary is
150 µμg m!
. Since the construction site is within a residential district as shown in Figure 3, the
secondary standard is dominant. As can be seen in Table 9, the influence should be concerned
can exist up to 200 meters; at the point of 100 meters, the concerned area is more than 40 meters
from the centerline. Figure 5 shows the distribution of the TSP concentrations in various
distances from the source and the centerline. The red curve represents the standard used in the
analysis.
Figure 5: TSP Distribution
-‐50
-‐40
-‐30
-‐20
-‐10
0
10
20
30
40
50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2
3
Distance
from
the
Centerline,
m
Distance
from
the
Source,
km
400-‐500
300-‐400
200-‐300
100-‐200
0-‐100
16. 16
2.3.2 PM10
In the same manner as described for the analysis of TSP, the emission rate of PM10 is
estimated at:
𝑄 = 𝑐!.! !"2𝜋𝜎! 𝜎! 𝑢
𝑄!"!" = 213.94 2𝜋 30𝑚 15𝑚 (1.43 𝑚/𝑠) = 865008.572 𝜇𝑔 𝑠
Thus, the PM10 distribution can be calculated and relevant results are shown in Table 10.
Table 10: PM10 Distribution Summary
PM10 Distributions,
𝛍𝐠 𝐦 𝟑
Distance, km
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 2 3
DistancefromtheCenterline,m
-50 53 39 28 16 11 5 3 2 1 1 0 0
-40 88 47 31 18 11 5 3 2 1 1 0 0
-30 130 54 33 19 12 6 3 2 1 1 0 0
-20 171 59 35 19 12 6 3 2 1 1 0 0
-10 202 63 37 20 12 6 3 2 1 1 0 0
0 214 64 37 20 12 6 3 2 1 1 0 0
10 202 63 37 20 12 6 3 2 1 1 0 0
20 171 59 35 19 12 6 3 2 1 1 0 0
30 130 54 33 19 12 6 3 2 1 1 0 0
40 88 47 31 18 11 5 3 2 1 1 0 0
50 53 39 28 16 11 5 3 2 1 1 0 0
According to the standard of PM10 shown in Table 3, within 24 hours, both of the
primary and secondary standards are 150 µμg m!
. Thus, the influence should be concerned
cannot reach 200 meters from the source in the centerline; for the point that is 100m from the
source in the centerline of the Gaussian Plume, the distance of the affected area exceeds 20
meters.
Based on the results, the PM10 distribution vs. the distance from the source and distance
from the Gaussian Plume centerline is concluded in Figure 6.
18. 18
Since there is no standard of PM2.5 in Brazil, the standard set by World Health
Organization (WHO), which is 25 µμg m!
, will be used. As can be seen in Table 10, the
concerned distance from the source along the centerline cannot reach 200 meters. At the point
100 meters from the source, the influenced distance off the line is more than 40 meters. The
results of PM2.5 distribution is shown in Figure 7.
Figure 7: PM2.5 Distribution
2.3.4 Carbon Monoxide (CO)
According to Table 6, the emission rate of carbon monoxide (CO) is estimated at
402.214g s, so the distribution can be determined and is shown in Table 11. Due to Table 3, the
standard for 8 hours is 10,000𝜇𝑔 𝑚!
; thus, the influenced area is within up to 300 meters, which
exceeds the size of the construction site, then some mitigations should be applied. The
distribution is also concluded in Figure 8.
-‐50
-‐40
-‐30
-‐20
-‐10
0
10
20
30
40
50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2
3
Distance
from
the
Centerline,
m
Distance
from
the
Source,
km
60-‐80
40-‐60
20-‐40
0-‐20
20. 20
2.3.5 Nitrogen Dioxide (NO2)
According to Table 6, the emission rate is 83.32 g/s; therefore, the distribution of
Nitrogen dioxide can be concluded in Table 12.
Table 12: Nitrogen dioxide Distribution Summary
NO2 Distributions,
𝛍𝐠 𝐦 𝟑
Distance, km
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 2 3
DistancefromtheCenterline,m
-50 5138 3750 2653 1589 1023 506 308 187 115 93 13 5
-40 8472 4489 2951 1705 1070 522 314 189 117 94 13 5
-30 12499 5164 3206 1801 1108 535 319 192 118 95 13 5
-20 16501 5707 3402 1872 1136 544 322 193 118 96 13 5
-10 19494 6060 3525 1917 1153 550 325 194 119 96 13 5
0 20607 6182 3567 1932 1159 552 325 195 119 96 13 5
10 19494 6060 3525 1917 1153 550 325 194 119 96 13 5
20 16501 5707 3402 1872 1136 544 322 193 118 96 13 5
30 12499 5164 3206 1801 1108 535 319 192 118 95 13 5
40 8472 4489 2951 1705 1070 522 314 189 117 94 13 5
50 5138 3750 2653 1589 1023 506 308 187 115 93 13 5
As can be seen in Table 12, since the standard shown in Table 3 is 190 𝜇𝑔 𝑚!
, the
influence of nitrogen dioxide is of significance. Along the centerline of the Gaussian Plume, the
influence can reach the distance more than 800 meters from the source. For the points
100/200/300/400/500/600/700 meters from the source in the centerline, the length of offline
points beyond the standard is more than 50 meters. In terms of the point 800 meters from the
source, the length is more than 30 meters. Thus, some corresponding mitigations should be
applied.
In addition, the distribution is also concluded in a diagram shown in Figure 9.
22. 22
According to Table 3, the Brazilian national secondary standard of Sulfur Dioxide is 100
𝜇𝑔 𝑚!
within 24 hours, so the influence can be more than 600 meters from the source along the
centerline of Gaussian Plume. For the points off the centerline, the distance between the last
offline point beyond the standard and the centerline is more than 50 meters. Thus, some
corresponding mitigations should be posed. The distribution is also shown in Figure 10.
Figure 10: Sulfur Dioxide Distribution
-‐50
-‐40
-‐30
-‐20
-‐10
0
10
20
30
40
50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2
3
Distance
from
the
Centerline,
m
Distance
from
the
Source,
km
300-‐400
200-‐300
100-‐200
0-‐100
23. 23
2.4 Analysis by Air Quality Health Index
According to the analysis by the Gaussian Plume, the concentrations of Nitrogen Dioxide
and PM2.5 along the centerline of the Gaussian Plume are determined. In terms of the ozone,
Table 14 shows the ozone level measured in a construction site.
Table 14: Ozone Produced During Construction [15]
The 0.12ppm is chosen as the estimated concentration of ozone in a typical construction
site. Use the same way explained in Session 2.3.1 to determine the emission rate of ozone, then
determine the distribution along the Gaussian Plume centerline. The concentrations of three
major elements in the AQHI formula are shown in Table 15.
Table 15: Concentrations of Ozone, Nitrogen Dioxide and PM2.5 along Centerline
Distance,
km
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 2 3
Ozone,
ppb
120 36 21 11 7 3 2 1 1 1 0 0
NO2,
ppb
1096 329 190 103 62 29 18 10 6 5 1 0
PM2.5,
𝝁𝒈 𝒎 𝟑 78 23 13 7 4 2 1 1 0 0 0 0
24. 24
Specifically, since the original unit of ozone is ppm, the conversion 1 ppm = 1 ppb is
used for its calculation. Similarly, the unit of NO2 discussed in Session 2.3.5, so the conversion 1
ppb of NO2 = 1.88 𝜇𝑔 𝑚!
of NO2 is used. For example, the concentration of NO2 at the
centerline point 100 m from the source is estimated at 2061𝜇𝑔 𝑚!
, then it should be conversed
to
!"#$
!.!!
= 1096 ppb.
Based on the values shown in Table 15, the Air Quality Health Index and corresponding
health risk level can be determined and it is summarized in Table 16 and Figure 11.
Table 16: Air Quality Health Index
Distance,
km
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 2 3
AQHI 16.38 3.48 1.90 0.99 0.59 0.27 0.16 0.09 0.06 0.05 0.00 0
Health
Risk
Very
High
Low Low Low Low Low Low Low Low Low Low Low
Figure 11: AQHI vs. Distance
As can be seen in Figure 11, the AQHI decreases dramatically with the distance from the
pollution increases. The most critical area is within 100 meters from the construction site.
-‐2.00
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
0
0.5
1
1.5
2
2.5
3
3.5
AQHI
Distance,
km
Very
High
Risk
High
Risk
Moderate
Risk
Low
Risk
25. 25
3.0 MITIGATIONS
As explained in Session 2.3, the emissions of three gas pollutants, namely carbon
monoxide, nitrogen dioxide and sulfur dioxide, should be mitigated. Due to the assumption
posed in Session 1.4, the construction site is a square and its length is 181 meters. Thus, the
purpose of the mitigations is to control the concentrations of these gas pollutants can satisfy the
atmospheric standards beyond 181 meters from the emission point. In order to facilitate the
engineering process, the centerline point 200 meters from the emission point should satisfy the
standards. Table 17 concludes the estimated concentrations of three pollutants and corresponding
standards.
Table 17: Estimated Pollutants Concentrations at the Edge of the Construction Site vs.
Standards
Emission Rate, g/s Concentrations, 𝜇𝑔 𝑚!
Standards, 𝜇𝑔 𝑚!
CO 402.21 g/s 29844 10000
NO2 83.32 g/s 6182 190
SO2 17.75 g/s 1317 100
In terms of the carbon monoxide, 𝜎! and 𝜎! are 50m and 30m at 0.2km from the emission
point; if c is 10000𝜇𝑔 𝑚!
, then:
𝑐 =
𝑄
2𝜋𝜎! 𝜎! 𝑢
𝑒
!
!!
!!!
!!
!!! !
!!!
!
10000 =
𝑄
2𝜋 50 30 1.43
𝑄 = 132,700,920 𝜇𝑔 𝑠 = 132.7 𝑔/𝑠
So the reduction rate should be:
𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑖𝑜!" =
402.21 − 132.70
402.21
= 67%
In the same manner, the objective reduction rates for these pollutants can be calculated
and summarized in Table 18
26. 26
Table 18: Objectives of Reduction Rates
Objective Reduction Rates
CO 67%
NO2 97%
SO2 93%
In order to achieve these objectives, here are some specific measures based on the
guideline Air Pollution Control at Construction Sites posed by Swiss Agency for the
Environment, Forests and Landscape (SAEFL) (2004).
Table 19: Mitigating Measures [16]
Construction
Activities
Mitigations
ThermalandChemicalWork
Processes
Restrict the thermal preparation of tar-based material and coating at building
sites
Use the asphalt or bitumen that has low emission rates of air pollutants
Use bitumen emulsions instead of bitumen solutions
Apply appropriate measures for binding material to lower the processing
temperature
In order to abate the welding emissions, it is necessary to capture, extract (e.g.
spot suction) and filter the emitted fumes.
When treat the surface or glue/seal the gaps, choose eco-friendly products
Deploy low emission explosives (e.g. formulated as emulsion, slurry or water
gel)
Stipulationsfor
MachinesAnd
Equipment
Use low-emission equipment (e.g. powered with electrical motors)
Equip and maintain combustion-engine powered tools/machines based on the
manufacturers’ specifications
Use low-sulfur fuels (sulfur content <50ppm) for machines and equipment to
power the diesel engines
Diesel-powered machines and equipment must be equipped with particle trap
systems (PTS) or other equivalently effective emission curtailment traps
Construction
Fulfillment
For scheduling, the contractor must submit the pertinent list before work
begins, and regularly update the list in order to ensure punctual availability of
the appropriate machines and equipment (a sample of pertinent list is shown
in Appendix A)
Train the workers about the origin, dispersal, impact and abatement of
airborne pollutants in order to promote the relevant awareness
27. 27
4.0 CONCLUSION
The purpose of this project is to determine distributions of air pollutants around a typical
construction site and provide corresponding mitigating measures. There are six air pollutants
being discussed in the report, namely Total Suspended Particulate (TSP), PM10, PM2.5, Carbon
Monoxide, Nitrogen Dioxide and Sulfur Dioxide. In the analysis, two methods are used, which
includes Gaussian Plume and Air Quality Health Index.
The construction site for the study is located in Salvador, Bahia, Brazil, (latitude
12°57’46” south, longitude 38°24’32” west) at an altitude of 34 m. As described in Session 1.4,
the construction site is assumed as a square and its length is approximately 200m. The emission
rates are estimated in the basis of the data obtained from an essay Identification and
Characterization of Particulate Matter Concentration at Construction Jobsites by Araujo, Costa,
and Moraes (2014) and a report Building Assemblies: Construction Energy & Emissions
conducted by University of British Columbia (1993).
In terms of results of Gaussian Plume method, carbon monoxide, nitrogen dioxide and
sulfur dioxide are three gas pollutants that should be controlled. For other three pollutants, their
influences should be concerned cannot reach 200m from the emission source, which is within the
construction site, so it is unnecessary to control the emission rates of these three pollutants.
When it comes to Air Quality Health Index (AQHI), the obvious risky area is within the
construction site, which indicates the residential districts around the construction site are not
effectively offended.
For mitigations, in order to decrease the constructions of carbon monoxide, nitrogen
dioxide and sulfur to the levels satisfying corresponding Brazilian Standards outside of the
construction area, some measures are provided in Session 4.0 in the basis of a guideline Air
28. 28
Pollution Control at Construction Sites posed by Swiss Agency for the Environment, Forests and
Landscape (SAEFL) (2004). Basically, the mitigating measures are divided into three categories
corresponding to three construction activities producing these three gas pollutants. Since the
limits of the resources, the decreases of emission rates caused by each measure cannot be
determined, only the objectives of reduction rates are calculated.
29. 29
REFERENCE
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Beychok, 1994.
[3] N. De Nevers, Air Pollution Control Engineering. William C Brown Pub, 2000.
[4] N. De Nevers, Air Pollution Control Engineering. William C Brown Pub, 2000.
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[Online]. Available: https://ec.gc.ca/cas-aqhi/default.asp?lang=En. [Accessed: 10-Apr-2015].
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http://transportpolicy.net/index.php?title=Brazil:_Air_Quality_Standards. [Accessed: 03-Apr-
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Particulate Matter Concentrations at Construction Jobsites’, Nov. 2014.
30. 30
[11] I. P. S. Araujo, D. B. Costa, and R. J. B. de Moraes, ‘Identification and Characterization of
Particulate Matter Concentrations at Construction Jobsites’, Nov. 2014.
[12] I. P. S. Araujo, D. B. Costa, and R. J. B. de Moraes, ‘Identification and Characterization of
Particulate Matter Concentrations at Construction Jobsites’, Nov. 2014.
[13] The Environmental Research Group, Building Assemblies: Construction Energy &
Emissions. Vancouver: University of British Columbia, 1993.
[14] I. P. S. Araujo, D. B. Costa, and R. J. B. de Moraes, ‘Identification and Characterization of
Particulate Matter Concentrations at Construction Jobsites’, Nov. 2014.
[15] Hazard Evaluation and Technical Assistance Branch of NIOSH, ‘Ozone Exposure at a
Construction Site’, Apr. 1999.
[16] A. Staubli and R. Kropf, Air Pollution Control at Construction Sites. Berne: Swiss Agency
for the Environment, Forests and Landscape, 2004.