Abstract:Motor vehicle traffic is an important source of particulate pollution in cities of the developing world, where rapid growth, coupled with a lack of effective transport and land use planning, may result in harmful levels of fine particles (PM2.5) in the air. However, a lack of air monitoring data hinders health impact assessments and the development of transportation and land use policies that could reduce health burdens due to outdoor air pollution. To address this important need, a study of traffic-related PM… Show more
“…The location of monitoring station with respect to the adjacent road has been found to be a main factor affecting the PM concentrations at classroom B. When compared with previous studies, it was found that in most of the outdoor measurements, the PM concentrations showed sharp drop with distance away from roadway (Kinney et al, 2011;Levy et al, 2003). Kinney et al (2011) observed that the average concentrations at the roadside (0 m) were more than 4 times higher than average concentrations 30 m away, and over 6 times higher than those 100 m away.…”
Section: Spatial Variations In Pm Concentrationsmentioning
confidence: 76%
“…When compared with previous studies, it was found that in most of the outdoor measurements, the PM concentrations showed sharp drop with distance away from roadway (Kinney et al, 2011;Levy et al, 2003). Kinney et al (2011) observed that the average concentrations at the roadside (0 m) were more than 4 times higher than average concentrations 30 m away, and over 6 times higher than those 100 m away. A Student's t test (single-tailed) was also performed to determine whether the PM 10 , PM 2.5 , and PM 1 concentrations were significantly higher at near-field region compared with far-field region.…”
Section: Spatial Variations In Pm Concentrationsmentioning
The PM 10 , PM 2.5 , and PM 1 (particulate matter with aerodynamic diameters <10, <2.5, and <1 mm, respectively) concentrations were monitored over a 90-day period in a naturally ventilated school building located at roadside in Chennai City. The 24-hr average PM 10 , PM 2.5 , and PM 1 concentrations at indoor and outdoor environments were found to be 136 AE 60, 36 AE 15, and 20 AE 12 and 76 AE 42, 33 AE 16, and 23 AE 14 mg/m 3 , respectively. The size distribution of PM in the classroom indicated that coarse mode was dominant during working hours (08:00 a.m. to 04:00 p.m.), whereas fine mode was dominant during nonworking hours (04:00 p.m. to 08:00 a.m.). The increase in coarser particles coincided with occupant activities in the classrooms and finer particles were correlated with outdoor traffic. Analysis of indoor PM 10 , PM 2.5 , and PM 1 concentrations monitored at another school, which is located at urban reserved forest area (background site) indicated 3-4 times lower PM 10 concentration than the school located at roadside. Also, the indoor PM 1 and PM 2.5 concentrations were 1.3-1.5 times lower at background site. Further, a mass balance indoor air quality (IAQ) model was modified to predict the indoor PM concentration in the classroom. Results indicated good agreement between the predicted and measured indoor PM 2.5 (R 2 ¼ 0.72-0.81) and PM 1 (R 2 ¼ 0.81-0.87) concentrations. But, the measured and predicted PM 10 concentrations showed poor correlation (R 2 ¼ 0.17-0.23), which may be because the IAQ model could not take into account the sudden increase in PM 10 concentration (resuspension of large size particles) due to human activities.Implications: The present study discusses characteristics of the indoor coarse and fine PM concentrations of a naturally ventilated school building located close to an urban roadway and at a background site in Chennai City, India. The study results will be useful to engineers and policymakers to prepare strategies for improving the IAQ inside classrooms. Further, this study may help in the development of IAQ standards and guidelines in India.
“…The location of monitoring station with respect to the adjacent road has been found to be a main factor affecting the PM concentrations at classroom B. When compared with previous studies, it was found that in most of the outdoor measurements, the PM concentrations showed sharp drop with distance away from roadway (Kinney et al, 2011;Levy et al, 2003). Kinney et al (2011) observed that the average concentrations at the roadside (0 m) were more than 4 times higher than average concentrations 30 m away, and over 6 times higher than those 100 m away.…”
Section: Spatial Variations In Pm Concentrationsmentioning
confidence: 76%
“…When compared with previous studies, it was found that in most of the outdoor measurements, the PM concentrations showed sharp drop with distance away from roadway (Kinney et al, 2011;Levy et al, 2003). Kinney et al (2011) observed that the average concentrations at the roadside (0 m) were more than 4 times higher than average concentrations 30 m away, and over 6 times higher than those 100 m away. A Student's t test (single-tailed) was also performed to determine whether the PM 10 , PM 2.5 , and PM 1 concentrations were significantly higher at near-field region compared with far-field region.…”
Section: Spatial Variations In Pm Concentrationsmentioning
The PM 10 , PM 2.5 , and PM 1 (particulate matter with aerodynamic diameters <10, <2.5, and <1 mm, respectively) concentrations were monitored over a 90-day period in a naturally ventilated school building located at roadside in Chennai City. The 24-hr average PM 10 , PM 2.5 , and PM 1 concentrations at indoor and outdoor environments were found to be 136 AE 60, 36 AE 15, and 20 AE 12 and 76 AE 42, 33 AE 16, and 23 AE 14 mg/m 3 , respectively. The size distribution of PM in the classroom indicated that coarse mode was dominant during working hours (08:00 a.m. to 04:00 p.m.), whereas fine mode was dominant during nonworking hours (04:00 p.m. to 08:00 a.m.). The increase in coarser particles coincided with occupant activities in the classrooms and finer particles were correlated with outdoor traffic. Analysis of indoor PM 10 , PM 2.5 , and PM 1 concentrations monitored at another school, which is located at urban reserved forest area (background site) indicated 3-4 times lower PM 10 concentration than the school located at roadside. Also, the indoor PM 1 and PM 2.5 concentrations were 1.3-1.5 times lower at background site. Further, a mass balance indoor air quality (IAQ) model was modified to predict the indoor PM concentration in the classroom. Results indicated good agreement between the predicted and measured indoor PM 2.5 (R 2 ¼ 0.72-0.81) and PM 1 (R 2 ¼ 0.81-0.87) concentrations. But, the measured and predicted PM 10 concentrations showed poor correlation (R 2 ¼ 0.17-0.23), which may be because the IAQ model could not take into account the sudden increase in PM 10 concentration (resuspension of large size particles) due to human activities.Implications: The present study discusses characteristics of the indoor coarse and fine PM concentrations of a naturally ventilated school building located close to an urban roadway and at a background site in Chennai City, India. The study results will be useful to engineers and policymakers to prepare strategies for improving the IAQ inside classrooms. Further, this study may help in the development of IAQ standards and guidelines in India.
“…Using the deposition fractions from fine PM, the estimated deposited masses are 9.5 µg in the head airways region, 1.1 µg in the tracheobronchial region and 0.7 µg in the pulmonary for every 1000 L of air breathed by an average urbanite in Nairobi city. However, Kinney et al (2011) reported PM 2.5 average concentration of 98.1 µg m -3 from a sidewalk in the Central Business District which translates to a deposition of 45 µg (head airways), 5.2 µg (tracheobronchial) and 3.0 µg (pulmonary). A study on occupational exposure by Ngo et al (2015) reported that bus drivers in Nairobi city were exposed to about 103 µg m -3 which translates to a deposition of 46 µg to the head airways, 5.5 µg to the tracheobronchial and 3.2 µg to the pulmonary tract.…”
Section: Discussionmentioning
confidence: 99%
“…The urban centres in Africa are growing at exceedingly high rates whereas the provision of prerequisite social services and amenities are not commensurate with this growth (UN-HABITAT, 2006;UNDESA, 2010;Karanja and Makau, 2012). The urban population is reported to be exposed to wide range of air pollutants (Gulis et al, 2004;Ngo et al, 2015) whose sources have been identified as vehicular emissions, biomass burning and mineral dust (Formenti et al, 2003;Gatari and Boman, 2003;Kinney et al, 2011;Petkova et al, 2013;Gaita et al, 2014). Therefore, the objectives of this study were to obtain a size distribution of PM and elemental concentrations in a typical African city and to determine the deposition fractions of measured PM and trace elements in the respiratory system.…”
Information from elemental and mass composition of size-fractionated airborne particle matter (PM) provides insightful knowledge about their impact on human health, meteorology and climate. To attain insight into the nature of sizefractionated PM from a typical African city, samples were collected from an urban background site in Nairobi, Kenya, during the months of August and September in 2007. PM samples ranging in size from 0.06 to 16 µm aerodynamic diameter were collected on pre-weighed polycarbonate filters with 0.4 µm pore size using a nine-stage cascade impactor. Particles less than 0.06 µm were collected on a backup filter. A total of 170 samples were collected and analysed for trace elements using the Proton Induced X-Ray Emission (PIXE) technique. The analysis showed that Si, Fe and S dominated in all size ranges and displayed unimodal mass-size distribution whereas K, Cu, Zn and Pb, depicted bimodal mass-size distribution highlighting the multiplicity of their sources. To estimate human exposure to PM, deposition fractions of both the coarse and fine PM in the human respiratory system were calculated. The deposited concentration was found to be highest in the head airways region compared to the tracheobronchial and pulmonary regions.
“…Toxic gases such as CO, NO x , SO x and PM x are in high concentrations as a result of increased urban expansion and motorization linked to rapid economic development experienced over the last decade. Consequently, most city residents are exposed to elevated concentrations of vehicular emissions that potentially pose serious long-term eff ects on human health and quality of the urban environment (Odhiambo et al, 2010;Kinney et al, 2011;Vliet & Kinney, 2007). For instance, CO causes blood clotting when it reacts with haemoglobin.…”
Abstract:The study uses contingent valuation (CV) framework to assess individuals' preferences for improved air quality management through motorized emission reductions in the city of Nairobi, Kenya. A conventional payment card (PC) is used to draw preferences from individuals in order to estimate the mean and the median willingness to pay (WTP) for air quality improvements in the city. Through interval regression analysis, the study finds that individuals are, on average, willing to pay Kshs. 396.57 ($4.67) and a median of Kshs. 244.94 ($2.88) to improve air quality management in the city. These amounts are found to increase with male gender, individuals' income, certainty about future income and residence in an urban area. These amounts, however, decline with age, residential distance from nearby roads, and motor vehicle ownership. On the whole, the study shows significant public support towards improved air quality management in the city, which is of vital importance for effective formation and implementation of air quality management programmes.
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