S.M. Shiva Nagendra

Line source emission modelling

Abstract Line source emission modelling is an important tool in control and management of vehicular exhaust emissions (VEEs) in urban environment. The US Environmental Protection Agency and many other research institutes have developed a number of line source models (LSMs) to describe temporal and spatial distribution of VEEs on roadways. Most of these models are either deterministic and/or statistical in nature. This paper presents a review of LSMs used in carrying out dispersion studies of VEEs, based on deterministic, numerical, statistical and artificial neural network techniques. The limitations associated with deterministic and statistical approach are also discussed.

Urban air quality management-A review

Urban air quality management plan (UAQMP) is an effective and efficient tool employed in managing acceptable urban air quality. However, the UAQM practices are specific to a country’s needs and requirements. Majority of the developed countries have full–fledged UAQMP with a regulatory management framework. However, developing countries are still working in formulating the effective and efficient UAQMPs to manage their deteriorating urban air environment. The first step in the process of formulation of UAQMP is to identify the air quality control regions based on ambient air quality status and second, initiate a time bound program involving all stakeholders to develop UAQMPs. The successful implementation of UAQMPs depends on the strength of its key components, e.g. goal/objective, monitoring network, emission inventory, air quality modeling, control strategies and public participation. This paper presents a comprehensive review on UAQMPs, being implemented worldwide at different scales e.g., national (macro), city (medium), and local (micro).

Application of positive matrix factorization in characterization of PM10 and PM2.5 emission sources at urban roadside

The 24-h average coarse (PM(10)) and fine (PM(2.5)) fraction of airborne particulate matter (PM) samples were collected for winter, summer and monsoon seasons during November 2008-April 2009 at an busy roadside in Chennai city, India. Results showed that the 24-h average ambient PM(10) and PM(2.5) concentrations were significantly higher in winter and monsoon seasons than in summer season. The 24-h average PM(10) concentration of weekdays was significantly higher (12-30%) than weekends of winter and monsoon seasons. On weekends, the PM(2.5) concentration was found to slightly higher (4-15%) in monsoon and summer seasons. The chemical composition of PM(10) and PM(2.5) masses showed a high concentration in winter followed by monsoon and summer seasons. The U.S.EPA-PMF (positive matrix factorization) version 3 was applied to identify the source contribution of ambient PM(10) and PM(2.5) concentrations at the study area. Results indicated that marine aerosol (40.4% in PM(10) and 21.5% in PM(2.5)) and secondary PM (22.9% in PM(10) and 42.1% in PM(2.5)) were found to be the major source contributors at the study site followed by the motor vehicles (16% in PM(10) and 6% in PM(2.5)), biomass burning (0.7% in PM(10) and 14% in PM(2.5)), tire and brake wear (4.1% in PM(10) and 5.4% in PM(2.5)), soil (3.4% in PM(10) and 4.3% in PM(2.5)) and other sources (12.7% in PM(10) and 6.8% in PM(2.5)).

Source characterization of PM10 and PM2.5 mass using a chemical mass balance model at urban roadside

The 24-h average ambient particulate matter (PM(10) and PM(2.5)) concentrations are sampled concurrently during November 2008-April 2009 at a busy roadside in Chennai City, India. The elemental (Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Se, Sr, Te, Tl, V and Zn) and ionic (Na(+), NH(4)(+), K(+), Ca(2+), Mg(2+), F(-), Cl(-), NO(2)(-), NO(3)(-) and SO(4)(2-)) composition of PM(10) and PM(2.5) are determined using an inductively coupled plasma-optical emission spectrometer (ICP-OES) and an ion chromatograph (IC), respectively. The emission inventory at the study area is also carried out to identify the likely PM emission sources. The U.S. EPAs-CMB (chemical mass balance) version 8.2 is applied to identify the source contribution of ambient PM(10) and PM(2.5) concentrations at the study area. Results indicated that diesel exhausts (43-52% in PM(10) and 44-65% in PM(2.5)) and gasoline exhausts (6-16% in PM(10) and 3-8% in PM(2.5)) are found to be the major source contributors at the study site followed by the paved road dusts (PM(10)=PM(2.5)=0.-2.3%), brake lining dusts (0.1% in PM(10) and 0.2% in PM(2.5)), brake pad wear dusts (0.1% in PM(10) and 0.01% in PM(2.5)), marine aerosols (PM(10)=PM(2.5)=0.1%) and cooking (~0.8% in PM(10) and ~1.5% in PM(2.5)).

PRINCIPAL COMPONENT ANALYSIS OF URBAN TRAFFIC CHARACTERISTICS AND METEOROLOGICAL DATA

Abstract Principal component analysis (PCA) is used to analyze one-year traffic, emission and meteorological data for an urban intersection in the Delhi. The 1997 data include meteorological, traffic and emission variables. In urban intersections the complexities of site, traffic and meteorological characteristic may result in a high cross correlation among the variables. In such situations, PCA can provide an independent linear combination of the variables. Here it is used to analyze 1, 8 and 24 h average emission, traffic and meteorological data. It shows that four principal components for the 24 h average have the highest loadings for traffic and emission variables with a strong correlation between them. PC loadings for the 1 and 8 h data indicate the least variation among them.

Chemical characterization of PM10 and PM2.5 mass concentrations emitted by heterogeneous traffic

In this paper, the chemical characterization of PM₁₀ and PM₂.₅ mass concentrations emitted by heterogeneous traffic in Chennai city during monsoon, winter and summer seasons were analysed. The 24-h averages of PM₁₀ and PM₂.₅ mass concentrations, showed higher concentrations during the winter season (PM₁₀=98 μg/m³; PM₂.₅=74 μg/m³) followed by the monsoon (PM₁₀=87 μg/m³; PM₂.₅=56 μg/m³) and summer (PM₁₀=77 μg/m³; PM₂.₅=67 μg/m³) seasons. The assessment of 24-h average PM₁₀ and PM₂.₅ concentrations was indicated as violation of the world health organization (WHO standard for PM₁₀=50 μg/m³ and PM₂.₅=25 μg/m³) and Indian national ambient air quality standards (NAAQS for PM₁₀=100 μg/m³ and PM₂.₅=60 μg/m³). The chemicals characterization of PM₁₀ and PM₂.₅ samples (22 samples) for each season were made for water soluble ions using Ion Chromatography (IC) and trace metals by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) instrument. Results showed the dominance of crustal elements (Ca, Mg, Al, Fe and K), followed by marine aerosols (Na and K) and trace elements (Zn, Ba, Be, Ca, Cd, Co, Cr, Cu, Mn, Ni, Pb, Se, Sr and Te) emitted from road traffic in both PM₁₀ and PM₂.₅ mass. The ionic species concentration in PM₁₀ and PM₂.₅ mass consists of 47-65% of anions and 35-53% of cations with dominance of SO₄²⁻ ions. Comparison of the metallic and ionic species in PM₁₀ and PM₂.₅ mass indicated the contributions from sea and crustal soil emissions to the coarse particles and traffic emissions to fine particles.

Characterizing and predicting coarse and fine particulates in classrooms located close to an urban roadway

The PM10, PM2.5, and PM1 (particulate matter with aerodynamic diameters <10, <2.5, and <1 μm, 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 PM10, PM2.5, and PM1 concentrations at indoor and outdoor environments were found to be 136 ± 60, 36 ± 15, and 20 ± 12 and 76 ± 42, 33 ± 16, and 23 ± 14 μg/m3, 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 PM10, PM2.5, and PM1 concentrations monitored at another school, which is located at urban reserved forest area (background site) indicated 3–4 times lower PM10 concentration than the school located at roadside. Also, the indoor PM1 and PM2.5 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 PM2.5 (R2 = 0.72–0.81) and PM1 (R2 = 0.81–0.87) concentrations. But, the measured and predicted PM10 concentrations showed poor correlation (R2 = 0.17–0.23), which may be because the IAQ model could not take into account the sudden increase in PM10 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.

Diurnal and seasonal variations of carbon monoxide and nitrogen dioxide in Delhi city

Motor vehicle exhaust emissions are one of the major causes of air quality deterioration in most of the cities of the developing world. Carbon monoxide (CO) and nitrogen dioxide (NO2) are significant contributors to this adverse effect on the environment. This study analyses air quality data for three years from 1997 to 1999, at two air quality control regions in Delhi city. The regions are a major traffic intersection and the moderately busy straight Khelgaon Marg road. The data were obtained from the Central Pollution Control Board (CPCB), Delhi. The results show that the highest ground-level concentrations of CO and NO2 occurred during winter (November to March) and the lowest during the tropical monsoon period (July to September) at both regions. Typical average monthly, weekly and diurnal cycles of CO at both regions have also been analysed, and show that CO concentrations are higher at the intersection than along the road. Further, the monthly average NO2 concentrations were also found to be higher at the intersection.

Urban heat island (UHI) influence on secondary pollutant formation in a tropical humid environment

ABSTRACT The combined action of urbanization (change in land use) and increase in vehicular emissions intensifies the urban heat island (UHI) effect in many cities in the developed countries. The urban warming (UHI) enhances heat-stress-related diseases and ozone (O3) levels due to a photochemical reaction. Even though UHI intensity depends on wind speed, wind direction, and solar flux, the thermodynamic properties of surface materials can accelerate the temperature profiles at the local scale. This mechanism modifies the atmospheric boundary layer (ABL) structure and mixing height in urban regions. These changes further deteriorate the local air quality. In this work, an attempt has been made to understand the interrelationship between air pollution and UHI intensity at selected urban areas located at tropical environment. The characteristics of ambient temperature profiles associated with land use changes in the different microenvironments of Chennai city were simulated using the Envi-Met model. The simulated surface 24-hr average air temperatures (11 m above the ground) for urban background and commercial and residential sites were found to be 30.81 ± 2.06, 31.51 ± 1.87, and 31.33 ± 2.1ºC, respectively. The diurnal variation of UHI intensity was determined by comparing the daytime average air temperatures to the diurnal air temperature for different wind velocity conditions. From the model simulations, we found that wind speed of 0.2 to 5 m/sec aggravates the UHI intensity. Further, the diurnal variation of mixing height was also estimated at the study locations. The estimated lowest mixing height at the residential area was found to be 60 m in the middle of night. During the same period, highest ozone (O3) concentrations were also recorded at the continuous ambient air quality monitoring station (CAAQMS) located at the residential area. Implications: An attempt has made to study the diurnal variation of secondary pollution levels in different study regions. This paper focuses mainly on the UHI intensity variations with respect to percentage of land use pattern change in Chennai city, India. The study simulated the area-based land use pattern with local mixing height variations. The relationship between UHI intensity and mixing height provides variations on local air quality.

Simulation of indoor comfort level in a building cooled by a cooling tower–concrete core cooling system under hot–semiarid climatic conditions

Concrete core cooling system is an energy efficient alternative to the conventional mechanical cooling system. It provides better comfort due to direct absorption of radiation load, low indoor air velocity, apt vertical temperature gradient and absence of noise. It can be operated at relatively higher water temperature, which facilitates the use of passive cooling strategies. In this study, a cooling tower, which is an ‘evaporative cooling system’, is preferred over other passive cooling options due to its better cooling performance in dry regions and its ability to operate all through the day. This paper presents the results of computational fluid dynamic analysis of a room cooled by concrete core cooling system supported by a cooling tower. The study reveals that for a typical hot–semiarid summer climatic condition in India, the system reduces the average indoor air temperature to a comfortable range of 23.5 to 28℃ from an uncomfortable range of 35.3 to 41℃ in a building without cooling. The average predicted percentage of dissatisfied falls from 99.7% in a building without cooling, to 37.3% if roof and floor of a building are cooled with concrete core cooling system and further to 6.3% if all surfaces are cooled with concrete core cooling system.