Mixing ratios of volatile organic compounds (VOCs) in the atmosphere of Karachi, Pakistan

Mixing ratios of carbon monoxide (CO), methane (CH4), non-methane hydrocarbons, halocarbons and alkyl nitrates (a total of 72 species) were determined for 78 whole air samples collected during the winter of 1998-1999 in Karachi, Pakistan. This is the first time that volatile organic compound (VOC) levels in Karachi have been extensively characterized. The overall air quality of the urban environment was determined using air samples collected at six locations throughout Karachi. Methane (6.3ppmv) and ethane (93ppbv) levels in Karachi were found to be much higher than in other cities that have been studied. The very high CH4 levels highlight the importance of natural gas leakage in Karachi. The leakage of liquefied petroleum gas contributes to elevated propane and butane levels in Karachi, although the propane and butane burdens were lower than in other cities (e.g., Mexico City, Santiago). High levels of benzene (0.3-19ppbv) also appear to be of concern in the Karachi urban area. Vehicular emissions were characterized using air samples collected along the busiest thoroughfare of the city (M.A. Jinnah Road). Emissions from vehicular exhaust were found to be the main source of many of the hydrocarbons reported here. Significant levels of isoprene (1.2ppbv) were detected at the roadside, and vehicular exhaust is estimated to account for about 20% of the isoprene observed in Karachi. 1,2-Dichloroethane, a lead scavenger added to leaded fuel, was also emitted by cars. The photochemical production of ozone (O3) was calculated for CO and the various VOCs using the Maximum Incremental Reactivity (MIR) scale. Based on the MIR scale, the leading contributors to O3 production in Karachi are ethene, CO, propene, m-xylene and toluene. © 2002 Elsevier Science Ltd. All rights reserved

Air pollution from motor vehicles a mathematical model analysis: case study in Ipoh City,Perak, Malaysia

Carbon Monoxide and Sulfur Dioxides concentrations and traffic volume data were collected at several locations in Ipoh City in the state of Perak in Malaysia. The sites were categorised into an enclosed area and an open area and at each site different vehicles driving mode were considered. A mathematical models based for pollutant concentration were developed using the least square method. Results from these studies indicate that the maximum concentration of pollutant is higher in an enclosed surrounding for all driving modes compared to an open surrounding similar traffic volume. The relationships derived between traffics flows and pollutants concentrations indicate that the adopted approach to forecast pollutant levels from traffic counts is workable for Malaysian situation. (Abstract by author

Distributional Fit of Carbon Monoxide Data

Air pollution is a problem that concerns many of us all over the world and it is a negative side effect of industrial development. Air pollution from cars and factories, in conjunction with a very humid climate, produce a highly corrosive environment. Land transportation provide a significant contribution to half of the total emission of PM 2.5, CO, HC and NOx, where air pollution levels have been exceeded or almost exceeded the ambient air quality standard. This study determine the distributional fit of carbon monoxide (CO) data obtained from Solar Energy Research Institute (SERI), Universiti Kebangsaan Malaysia, Bangi from 16 September 2008 to 16 January 2009. The distribution models used in this study were exponential, gamma, generalized extreme value, lognormal and Weibull distributions. Parameters for all distribution models were estimated by using maximum likelihood method. The goodness of fit of the models were determined by using Kolmogorov-Smirnov and Anderson Darling statistics. The lognormal distribution model was found to fit better than other distribution models. Key-Words: - Statistical distribution models, air pollution, maximum likelihood method, goodness of fit tests

Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 µm2 cm−3) site and an urban background (UB, average LDSA = 11.2 ± 7.1 µm2 cm−3) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination (R2), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 µm (PM2.5), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM2.5, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (R2=0.80, MAE = 3.7 µm2 cm−3) than at the UB site (R2=0.77, MAE = 2.3 µm2 cm−3), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations.Peer reviewe

Monitoring and modelling of non methane hydrocarbons (NMHCs) in various areas in Pulau Pinang, Malaysia.

Hidrokarbon bukan metana (NMHC) memainkan peranan penting dalam proses pembentukan ozon dalam persekitaran bandar, di mana pembebasan dari asap kenderaan adalah dominan. Non Methane Hydrocarbons (NMHC) plays a vital role in the formation process of ozone in urban environment, where vehicle emissions are dominant

Exposure Assessment for Atmospheric Ultrafine Particles (UFPs) and Implications in Epidemiologic Research

Epidemiologic research has shown increases in adverse cardiovascular and respiratory outcomes in relation to mass concentrations of particulate matter (PM) ≤2.5 or ≤10 μm in diameter (PM(2.5), PM(10), respectively). In a companion article [Delfino RJ, Sioutas C, Malik S. 2005. Environ Health Perspect 113(8):934–946]), we discuss epidemiologic evidence pointing to underlying components linked to fossil fuel combustion. The causal components driving the PM associations remain to be identified, but emerging evidence on particle size and chemistry has led to some clues. There is sufficient reason to believe that ultrafine particles < 0.1 μm (UFPs) are important because when compared with larger particles, they have order of magnitudes higher particle number concentration and surface area, and larger concentrations of adsorbed or condensed toxic air pollutants (oxidant gases, organic compounds, transition metals) per unit mass. This is supported by evidence of significantly higher in vitro redox activity by UFPs than by larger PM. Although epidemiologic research is needed, exposure assessment issues for UFPs are complex and need to be considered before undertaking investigations of UFP health effects. These issues include high spatial variability, indoor sources, variable infiltration of UFPs from a variety of outside sources, and meteorologic factors leading to high seasonal variability in concentration and composition, including volatility. To address these issues, investigators need to develop as well as validate the analytic technologies required to characterize the physical/chemical nature of UFPs in various environments. In the present review, we provide a detailed discussion of key characteristics of UFPs, their sources and formation mechanisms, and methodologic approaches to assessing population exposures

Derivation of Black Carbon Proxies in an Integrated Urban Air Quality Monitoring Network

Air pollution is one of the biggest environmental health challenges in the world, especially in the urban regions where about 90% of the world’s population lives. Black carbon (BC) has been demonstrated to play an important role in climate change, air quality and potential risk for human beings. BC has also been suggested to associate better with health effects of aerosol particles than the commonly monitored particulate matter, which does not solely originate from combustion sources. Furthermore, BC has been recommended to be included as one of the parameters in air quality index (AQI) which is communicated to citizens. However, due to financial constraints and the lack of the national legislation, BC has yet been measured in every air quality monitoring station. Therefore, some researchers developed low-cost sensors which give indicative ambient BC concentrations as an alternative. Even so, due to instrument failure or data corruption, measurements by physical sensors are not always possible and long data gaps can exist. With missing data, the data analysis of interactions between air pollutants becomes more uncertain; therefore, air quality models are needed for data gap imputation and, moreover, for sensor virtualization. To complement the current deficiency, this thesis aims to derive statistical proxies as virtual sensors to estimate BC by using the current air quality monitoring network in Helsinki metropolitan area (HMA). To achieve this, we first characterized the ambient BC concentrations in four types of environments in HMA: traffic site (TR: 0.77–2.08 μg m−3), urban background (UB: 0.51–0.53 μg m−3), detached housing (DH: 0.64–0.80 μg m−3) and regional background (RB: 0.27–0.28 μg m−3). TR, in general, had higher BC concentrations due to the close proximity to vehicular emission but decreasing trends (–10.4 % yr−1) likely thanks to the fast renewal of the city bus fleet in HMA. UB, on the other hand, had a more diverse source of BC, including biomass burning and traffic combustion. Its trend had also been decreasing, but at a smaller rate (e.g. UB1: –5.9 % yr−1). We then narrowed down the dataset to a street canyon site and an urban background site for BC proxy derivation. At both sites, despite the low correlation with meteorological factors, BC correlated well with other commonly monitored air pollutant parameters by both reference instruments and low-cost sensors, such as NOx and PM2.5. Based on this close association, we developed a statistical proxy with adaptive selection of input variables, named input-adaptive proxy (IAP). This white-box model worked better in terms of accuracy at the street canyon site (R2 = 0.81–0.87) than the urban background site (R2 = 0.44–0.60) because of the scarce missing gaps in data in the street canyon. When compared with other white- and black-box models, IAP is preferred because of its flexibility and architectural transparency. We further demonstrated the feasibility of sensor virtualization by using statistical proxies like IAP at both sites. We also stressed that such virtual sensors are location specific, but it might be possible to extend the models from one street canyon site to another with a calibration factor. Similarly, the proposed methodology can also be applied to estimate other air pollutant parameters with scarcity of data, such as lung deposited surface area and ultrafine particles, to complement the existing AQI.