Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates

Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196 m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23 m and reached 82 m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100 m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015 : a systematic analysis for the Global Burden of Disease Study 2015

Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography-year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61.7 years (95% uncertainty interval 61.4-61.9) in 1980 to 71.8 years (71.5-72.2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11.3 years (3.7-17.4), to 62.6 years (56.5-70.2). Total deaths increased by 4.1% (2.6-5.6) from 2005 to 2015, rising to 55.8 million (54.9 million to 56.6 million) in 2015, but age-standardised death rates fell by 17.0% (15.8-18.1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14.1% (12.6-16.0) to 39.8 million (39.2 million to 40.5 million) in 2015, whereas age-standardised rates decreased by 13.1% (11.9-14.3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42.1%, 39.1-44.6), malaria (43.1%, 34.7-51.8), neonatal preterm birth complications (29.8%, 24.8-34.9), and maternal disorders (29.1%, 19.3-37.1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000-183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000-532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Copyright (C) The Author(s). Published by Elsevier Ltd.Peer reviewe

Modelling the impact of traffic emissions on local air quality

THESIS 8599Air quality modelling can be used to complement monitoring networks and to obtain information at lower cost. A primary advantage of modelling is that it can be used for the prediction of future air quality, which often forms an important part of Environmental Impact Assessment (EIA) studies. The research described in this thesis represents an in - depth atmospheric dispersion modelling study carried out at highway and street canyon locations in the Dublin area for the pollutants CO, NOx and NO2. In this context, appropriate dispersion models GFLSM and CALINE4 for highways and STREET and OSPM models were studied and validated for local conditions

The Air Quality Assessment of Northern Hilly City in India.

The last decade in India has seen a rapid deterioration in the air quality in its major cities. This has led to increased interest from the general public to their exposure to ambient air quality primarily because of the effects of such air pollutants on human health. In this context, the air quality indices (AQI) is often used by the local authorities to signify the levels of the seriousness of air pollution to the common public. The use of air quality indexing for assessment of existing air quality standards has been widely used for different cities in India and the world. The paper presents the application of air quality indices for assessing the existing air quality standards in an Indian city, Shimla. The indices have been calculated using the methodology described by the US Environmental Protection Agency (USEPA), which is adopted by the Central Pollution Control Board (CPCB) in India. An alternative method for determination of air quality indices is also utilized (referred to as AQIam for the Indian context. The estimates air quality indices are applied to two monitoring sites (Tekka Bench, Ridge and ISBT bus stand) in Shimla city over the study period (2004-2015) on the pollutants: Sulphur dioxide (SO2), oxides of nitrogen (NOx), suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM). The annual air quality indices results for the study period showed that the air quality was 'good' for Tekka Bench monitoring station for the entire study period and for the ISBT bus stand for all the years, except 2011 when it was in ‘moderate' category. The annual air quality indices predicted using the alternative methodology indicated the level of air quality to be 'good' for the entire study period, except 2013 when it was classified as ‘satisfactory' for the monitoring site at Tekka Bench. Similarly, the annual air quality was classified as 'moderate’ for the years 2011, 2013-2015 for the monitoring station at ISBT bus stand site with the remaining years of the study period being classified as 'good'. These categorizations of existing air quality interpret the expected health effects of exposure to surrounding ambient air. Higher the value of air quality indices more severe is the categorization and thereby more harmful are the human health effects being exposed to ambient air conditions. Similar such seasonal variations of air quality indices were also observed during the study period at both the monitoring sites

The Air Quality Assessment of Northern Hilly City in India.

The last decade in India has seen a rapid deterioration in the air quality in its major cities. This has led to increased interest from the general public to their exposure to ambient air quality primarily because of the effects of such air pollutants on human health. In this context, the air quality indices (AQI) is often used by the local authorities to signify the levels of the seriousness of air pollution to the common public. The use of air quality indexing for assessment of existing air quality standards has been widely used for different cities in India and the world. The paper presents the application of air quality indices for assessing the existing air quality standards in an Indian city, Shimla. The indices have been calculated using the methodology described by the US Environmental Protection Agency (USEPA), which is adopted by the Central Pollution Control Board (CPCB) in India. An alternative method for determination of air quality indices is also utilized (referred to as AQIam for the Indian context. The estimates air quality indices are applied to two monitoring sites (Tekka Bench, Ridge and ISBT bus stand) in Shimla city over the study period (2004-2015) on the pollutants: Sulphur dioxide (SO2), oxides of nitrogen (NOx), suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM). The annual air quality indices results for the study period showed that the air quality was 'good' for Tekka Bench monitoring station for the entire study period and for the ISBT bus stand for all the years, except 2011 when it was in ‘moderate' category. The annual air quality indices predicted using the alternative methodology indicated the level of air quality to be 'good' for the entire study period, except 2013 when it was classified as ‘satisfactory' for the monitoring site at Tekka Bench. Similarly, the annual air quality was classified as 'moderate’ for the years 2011, 2013-2015 for the monitoring station at ISBT bus stand site with the remaining years of the study period being classified as 'good'. These categorizations of existing air quality interpret the expected health effects of exposure to surrounding ambient air. Higher the value of air quality indices more severe is the categorization and thereby more harmful are the human health effects being exposed to ambient air conditions. Similar such seasonal variations of air quality indices were also observed during the study period at both the monitoring sites

Assessment of landfill gases by LandGEM and energy recovery potential from municipal solid waste of Kanpur city, India

The world due to increased urbanization and globalization is facing major environmental challenges. Anthropogenic emissions of Greenhouse gases (GHG) like carbon dioxide and methane are on the rise and unsustainable which needs to be regulated. Open dumping of Municipal Solid Waste (MSW) contributes to generation of greenhouse gases like carbon dioxide and methane. This is because large fractions of the waste open dumped are organic in nature which undergoes anaerobic decomposition leading to generation of GHGs. In particular, methane has a high potential for energy generation and if utilized could be highly beneficial. The present study assesses the generation of landfill gases, primarily methane generation potential from MSW generated in Kanpur city using LandGEM 3.02 version model developed by USEPA for the period 2015–2030. It was observed from the study that the cumulative LFGs generation, methane emission and energy recovery potential estimated as 233.44 × 106 m3, 116 × 106 m3 and 858.14 × 106 MJ respectively. Uncertainty analysis carried out showed that variation in methane emissions maybe attributed to input parameters of k and Lo of the LandGEM model. The study shows that there exists high potential to control the greenhouse gas emissions by utilizing the methane generated for energy production

Parametric Analysis of Leachate and Water Resources around Municipal Solid Waste Landfill area in Solan

Leachate is defined as the liquid that drains from the landfill. The paper presents the physico-chemical, bacteriological and heavy metal testing results carried out for leachate, surface and sub-surface water samples collected from municipal solid waste landfill and different water sources in Solan to find out the effect of leachate percolation on groundwater quality. Physico-chemical parameters analysed were, pH, Total Dissolve Solid (TDS), sulphate, turbidity, Electrical Conductivity (EC) while biological parameters tested were Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Most Probable Number (MPN) test and ammonical nitrogen. Testing for heavy metals (Pb, Zn, Cr, Ni, Fe) were carried out and have been reported. The results reveal that the leachate from the unlined landfill may have a significant impact on the groundwater resource (often used as drinking source) particularly because of the toxic nature of the leachate coupled with the soil characteristics which is permeable in nature

Parametric Analysis of Leachate and Water Resources around Municipal Solid Waste Landfill area in Solan

Leachate is defined as the liquid that drains from the landfill. The paper presents the physico-chemical, bacteriological and heavy metal testing results carried out for leachate, surface and sub-surface water samples collected from municipal solid waste landfill and different water sources in Solan to find out the effect of leachate percolation on groundwater quality. Physico-chemical parameters analysed were, pH, Total Dissolve Solid (TDS), sulphate, turbidity, Electrical Conductivity (EC) while biological parameters tested were Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Most Probable Number (MPN) test and ammonical nitrogen. Testing for heavy metals (Pb, Zn, Cr, Ni, Fe) were carried out and have been reported. The results reveal that the leachate from the unlined landfill may have a significant impact on the groundwater resource (often used as drinking source) particularly because of the toxic nature of the leachate coupled with the soil characteristics which is permeable in nature

Trend Analysis of observational PM₁₀ concentrations in Shimla city, India

The present study aims to evaluate the long-term trends of PM10 at two monitoring stations (an urban and a background station) in Shimla city, in India during the period 2011-2017. The highest daily mean concentrations were determined to be 176μg/m³ and 152μg/m³ respectively at the urban and the background monitoring locations. Similarly, the annual mean concentrations at the monitoring locations were determined to be 59μg/m³ and 45μg/m³ respectively for urban and background concentrations. Exceedance factors determined showed that at the urban monitoring location the ranges varied between ‘moderate to high’ while at the background monitoring station it remained at ‘moderate’ levels. Seasonal analysis study carried out revealed that higher concentrations were observed during summer in comparison to winter with the least concentrations occurring during the monsoon season. A regression analysis was carried out to test the interdependency of the PM10 with other pollutants and a positive correlation was observed between PM10 and NO₂ and SO₂. Similarly, correlation of PM10 with meteorological parameters such as wind speed and temperature were found to be positive while for parameters like precipitation and relative humidity it was negative. The paper also presents a critical discussion on the outcomes of the trend analysis study. This includes design and location of additional monitoring sites to adequately represent the actual ambient air quality conditions in Himachal Pradesh

Trend analysis of observational PM10 concentrations in Shimla city, India

The present study aims to evaluate the long-term trends of PM10 at two monitoring stations (an urban and a background station) in Shimla city, in India during the period 2011–2017. The highest daily mean concentrations were determined to be 176 μg/m³ and 152 μg/m³ respectively at the urban and the background monitoring locations. Similarly, the annual mean concentrations at the monitoring locations were determined to be 59 μg/m³ and 45 μg/m³ respectively for urban and background concentrations. Exceedance factors determined showed that at the urban monitoring location the ranges varied between ‘moderate to high’ while at the background monitoring station it remained at ‘moderate’ levels. Seasonal analysis study carried out revealed that higher concentrations were observed during summer in comparison to winter with the least concentrations occurring during the monsoon season. A regression analysis was carried out to test the interdependency of the PM10 with other pollutants and a positive correlation was observed between PM10 and NO₂ and SO₂. Similarly, correlation of PM10 with meteorological parameters such as wind speed and temperature were found to be positive while for parameters like precipitation and relative humidity it was negative. The paper also presents a critical discussion on the outcomes of the trend analysis study. This includes design and location of additional monitoring sites to adequately represent the actual ambient air quality conditions in Himachal Pradesh