Real-world tailpipe emissions from autorickshaws (3-wheelers) under heterogeneous traffic conditions.

The current study aimed to measure real-world emissions of three-wheeled autorickshaws powered by CNG and parameters (such as speed, acceleration, air-fuel (A/F) ratio, and rpm) influencing 3-wheeler emission rates. Test vehicles manufactured under Bharat Standards BS-III and BS-IV were monitored for exhaust emissions in Delhi city using a portable exhaust emission measurement system (AVL Ditest Gas 1000). The average emission rates of CO, HC, and NO gases for on-road autorickshaws were found to be 0.015 ± 0.017, 0.003 ± 0.0017, and 0.007 ± 0.005 g/s, respectively. Further, the highest emission factor values of 3.98 g/km and 3.93 g/km were estimated for CO and HC+NO gases, respectively. These values were found to be 1.4-3.2 times higher than the respective BS emission norms (BS III-CO =1.25 g/km, HC+NO = 1.25 g/km; BS-IV-CO = 0.94 g/km and HC+NO = 0.94 g/km). In this study, it was observed that the driving pattern and emissions were affected by traffic characteristics, driver behavior (constant acceleration and deceleration), and vehicle characteristics. The air-fuel ratio (A/F) was found to correlate highly with emission rates, followed by acceleration/deceleration and speed. Further analysis found that more than 70% of the aggregated emissions were due to acceleration and deceleration, which contributed to nearly 70% of the travel time. This was followed by the breakdown of speed and emissions into different bins, which found that 20-30 kmph has a higher emission rate and 40-50 kmph bin has a lower emission rate.

Trends, Extreme Events and Long-term Health Impacts of Particulate Matter in a Southern Indian Industrial Area.

The present study uses various statistical tools to understand the behaviour of PM2.5 and PM10 in the Kanjikode industrial area of Southern India. Annual PM2.5 and PM10 average concentrations in 2018-2020 were three times more than the World Health Organization-specified standards (5 and 15 µg m-3). The statistical distribution analysis suggested well-fitted lognormal and gamma distributions of 24-h average PM2.5 concentrations and gamma distributions of 24-h average PM10 concentrations. Trend analysis observed a notable monotonic increasing trend for 24-h average PM2.5 concentrations with an increasing magnitude of 0.43 µg m-3 per annum. A downward trend was found for 24-h average PM10 concentrations, with a decreasing magnitude of 0.2 µg m-3 per year. Extreme event analysis of PM2.5 and PM10 has provided the highest concentration levels expected in the coming 10 years, 193 and 165 µg m-3, respectively, higher than the Indian National Ambient Air Quality Standards and considered a public health threat. The health risk assessment by AirQ + emphasized that more than 15, 34, and 27 premature deaths caused by total mortality in 2018, 2019, and 2020 could have been prevented if PM2.5 concentrations in the Kanjikode industrial area did not exceed 10 µg m-3. Statistical analysis and health risk assessment suggested adopting various constructive and multipronged approaches to reduce pollution levels and develop a health risk management plan in the industrial region. Supplementary Information: The online version contains supplementary material available at 10.1007/s11270-023-06302-y.

Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?

The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m-3 (lockdown) versus 81 µg m-3 (pre-lockdown); PM10: 171 µg m-3 versus 235 µg m-3; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 µg m-3 versus 39 µg m-3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.

Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art.

Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.

Dynamics of PM2.5 pollution in the vicinity of the old municipal solid waste dumpsite.

The present study explored the effect of local meteorology on the dispersion of PM2.5 from a 30-year open municipal solid waste (MSW) dumpsite in Chennai, India. The spatial monitoring was conducted in and around the dumpsite to understand the impacts of dumpsite activities on the nearby residential area. Results showed that dumpsite activities are responsible for deteriorating local air quality. The 24-h average PM2.5 concentrations were 50, 43.7, and 34 µg m-3 during stagnation, recirculation, and ventilation events, respectively. Spearman's correlation showed an inverse relationship between PM2.5 and temperature; wind speed indicated dispersion of fine aerosols. The observed inverse relationship between PM2.5 and relative humidity indicated the hygroscopic growth of fine aerosols in the study area. We used AERMOD to simulate the dispersion of 1-h, 8-h, and 24-h PM2.5 emissions from open waste burning in the dumpsite. The 1-h, 8-h, and 24-h simulated results showed the maximum concentration of 247, 136, and 53.4 µg m-3 in the dumpsite, and concentration levels ranged between 50-60, 30-50, and 10-20 µg m-3 were observed in the nearby residential area. The AERMOD predictions indicated that open waste burning could be a significant contributor to high PM2.5 concentration in an adjacent residential area of the dumpsite.

Unprecedented reduction in air pollution and corresponding short-term premature mortality associated with COVID-19 lockdown in Delhi, India.

Countries around the world introduced strict restrictions on movement and activities known as 'lockdowns' to restrict the spread of the novel coronavirus disease (COVID-19) from the end of 2019. A sudden improvement in air quality was observed globally as a result of these lockdowns. To provide insight into the changes in air pollution levels in response to the COVID-19 restrictions we have compared surface air quality data in Delhi during four phases of lockdown and the first phase of the restriction easing period (25 March to 30 June 2020) with data from a baseline period (2018-2019). Simultaneously, short-term exposure of PM2.5 and O3 attributed premature mortality were calculated to understand the health benefit of the change in air quality. Ground-level observations in Delhi showed that concentrations of PM10, PM2.5 and NO2 dropped substantially in 2020 during the overall study period compared with the same period in previous years, with average reductions of ~49%, ~39%, and ~39%, respectively. An overall lower reduction in O3 of ~19% was observed for Delhi. A slight increase in O3 was found in Delhi's industrial and traffic regions. The highest peak of the diurnal variation decreased substantially for all the pollutants at every phase. The decrease in PM2.5 and O3 concentrations in 2020, prevented 904 total premature deaths, a 60% improvement when compared to the figures for 2018-2019. The restrictions on human activities during the lockdown have reduced anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world.Implications: I am submitting herewith the manuscript entitled "Unprecedented Reduction in Air Pollution and Corresponding Short-term Premature Mortality Associated with COVID-19 Forced Confinement in Delhi, India" for potential publishing in your journal.The novelty of this research lies in: (1) we utilized ground-level air quality data in Delhi during four phases of lockdown and the first phase of unlocking period (25th March to 30th June) for 2020 as well as data from the baseline period (2018-2019) to provide an early insight into the changes in air pollution levels in response to the COVID-19 pandemic, (2) Chatarize the change of diurnal variation of the pollutants and (3) we assess the health risk due to PM2.5 and O3. Results from ground-level observations in Delhi showed that concentrations of PM10, PM2.5 and NO2 substantially dropped in 2020 during the overall study period compared to the similar period in previous years, with an average reduction of ~49%, ~39%, and ~39%, respectively. In the case of O3, the overall reduction was observed as ~19% in Delhi, while a slight increase was found in industrial and traffic regions. And consequently, the highest peak of the diurnal variation decreased substantially for all the pollutants. The health impact assessment of the changes in air quality indicated that 904 short-term premature deaths (~60%) were prevented due to the decline in PM2.5 and O3 concentrations in the study period. The restrictions on human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world.

Advances in Mid-Infrared Spectroscopy-Based Sensing Techniques for Exhaled Breath Diagnostics.

Human exhaled breath consists of more than 3000 volatile organic compounds, many of which are relevant biomarkers for various diseases. Although gas chromatography has been the gold standard for volatile organic compound (VOC) detection in exhaled breath, recent developments in mid-infrared (MIR) laser spectroscopy have led to the promise of compact point-of-care (POC) optical instruments enabling even single breath diagnostics. In this review, we discuss the evolution of MIR sensing technologies with a special focus on photoacoustic spectroscopy, and its application in exhaled breath biomarker detection. While mid-infrared point-of-care instrumentation promises high sensitivity and inherent molecular selectivity, the lack of standardization of the various techniques has to be overcome for translating these techniques into more widespread real-time clinical use.

Characterization and health risk assessment of indoor dust in biomass and LPG-based households of rural Telangana, India.

Indoor dust is one of the key sources contributing to indoor air pollution (IAP) in rural households. It acts as a media for various toxicants like heavy metal depositions and causes severe health risks. The present study deals with investigation of metal concentrations and morphological characteristics of indoor dust generated in varied fuel types followed by estimation of health risks for women and children in rural households in Telangana, India. Indoor floor dust samples were collected from households using biomass and liquefied petroleum gas (LPG) as their cooking energy during winter to evaluate the morphological and chemical characteristics in the aforementioned dust samples. A morphological (SEM-EDX) analysis revealed the presence of carbonaceous particles in biomass-based households and mineral-rich crustal sources in LPG-based households. As observed from ICP-OES analysis, there is a significant difference in mean concentrations of Al, Co, Cr, Fe, Zn, and Ni based on fuel type, except for Mn and Pb. From Pearson's correlation analysis and principal component analysis, it was observed that the biomass households were dominated by Zn, Al, Mn, Cr, and Pb, which could have been contributed from biomass burning deposits, crustal sources, and unpaved roads, while Cr, Pb, Fe, and Mn dominated in LPG households, indicating their origin from leaded paints (Pb and Cr) and crustal sources. The health risks associated with these heavy metals to women and children were investigated using an EPA health risk model. The values from the model indicated that both non-carcinogenic and carcinogenic risks were within the safe levels for both subjects. This study not only establishes chemical and morphological characteristics of indoor dust, but also quantifies the role of fuel type.Implications: The present study provides the latest geographical evidence of chemical and morphological characterization of indoor dust particles in varied fuels; i.e, biomass- and LPG-based households and associated health risk assessment in a sub-tropical rural site in Telangana, India. Nevertheless, further research is essential from various regions across the country for more heavy metal analysis and factors impacting these levels. One of the major limitations of the present study is the analysis of few metals and measurements in only living area locations. Future studies can include soil and road dust, as well as kitchens and bedrooms, to provide more comprehensive analysis of dust compositions in varied environments.

Ozone pollution in Chinese cities: Assessment of seasonal variation, health effects and economic burden.

The ground-level ozone (O3) concentration in the urban regions of China has become an increasingly noticeable environmental problem in recent years. Many epidemiological studies have reported the association between O3 pollution and mortality, only a few studies have focused on the O3-related mortality and corresponding economic effects at the Chinese city and province level. This study reports the seasonal variation of ground-level O3 in 338 cities of China during the year 2016 and evaluates its effect on premature mortality and economic loss. It further illustrates the differences in cause-specific mortality outcomes of the log-linear and linear model, two of the prominently used methods for estimating health effects. In 2016, the annual average daily maximum 8-h O3 concentration in China ranged between 74 and 201 µg/m3 (138 ±â€¯24.7 µg/m3). 30% of the total population was exposed to >160 µg/m3 O3 concentration (Chinese national ambient air quality standard) and about 67.2% urban population lived in exposure above the WHO recommended O3 concentrations (100 µg/m3). The estimated national O3-attributable mortality was 74.2 × 103 (95% CI: 16.7×103-127×103) in the log-linear model, whereas, the total O3-related mortality using the linear model was 69.6 × 103 (95% CI: 16.2 × 103-115 × 103). The exposure to O3 caused a nationwide economic loss of about 7.6 billion US$ (range: 1.7-12.9) in 2016. This study uniquely provides most comprehensive coverage of the Chinese cities for O3 associated mortality utilizing ground level measurement data for 2016 and presents a measurable assessment to the policymakers of China for streamlining their efforts on air quality improvement and O3 containment.

Urban local air quality management framework for non-attainment areas in Indian cities.

Increasing urban air pollution level in Indian cities is one of the major concerns for policy makers due to its impact on public health. The growth in population and increase in associated motorised road transport demand is one of the major causes of increasing air pollution in most urban areas along with other sources e.g., road dust, construction dust, biomass burning etc. The present study documents the development of an urban local air quality management (ULAQM) framework at urban hotspots (non-attainment area) and a pathway for the flow of information from goal setting to policy making. The ULAQM also includes assessment and management of air pollution episodic conditions at these hotspots, which currently available city/regional-scale air quality management plans do not address. The prediction of extreme pollutant concentrations using a hybrid model differentiates the ULAQM from other existing air quality management plans. The developed ULAQM framework has been applied and validated at one of the busiest traffic intersections in Delhi and Chennai cities. Various scenarios have been tested targeting the effective reductions in elevated levels of NOx and PM2.5 concentrations. The results indicate that a developed ULAQM framework is capable of providing an evidence-based graded action to reduce ambient pollution levels within the specified standard level at pre-identified locations. The ULAQM framework methodology is generalised and therefore can be applied to other non-attainment areas of the country.

Personal exposure to fine particulate matter concentrations in central business district of a tropical coastal city.

In the present study, personal exposure to fine particulate matter (particulate matter with an aerodynamic diameter <2.5 µm [PM2.5]) concentrations in an urban hotspot (central business district [CBD]) was investigated. The PM monitoring campaigns were carried out at an urban hotspot from June to October 2015. The personal exposure monitoring was performed during three different time periods, i.e., morning (8 a.m.-9 a.m.), afternoon (12.30 p.m.-1.30 p.m.), and evening (4 p.m.-5 p.m.), to cover both the peak and lean hour activities of the CBD. The median PM2.5 concentrations were 38.1, 34.9, and 40.4 µg/m3 during the morning, afternoon, and evening hours on the weekends. During weekdays, the median PM2.5 concentrations were 59.5, 29.6, and 36.6 µg/m3 in the morning, afternoon, and evening hours, respectively. It was observed that the combined effect of traffic emissions, complex land use, and micrometeorological conditions created localized air pollution hotspots. Furthermore, the total PM2.5 lung dose levels for an exposure duration of 1 hr were 8.7 ± 5.7 and 12.3 ± 5.2 µg at CBD during weekends and weekdays, respectively, as compared with 2.5 ± 0.8 µg at the urban background (UB). This study emphasizes the need for mobile measurement for short-term personal exposure assessment complementing the fixed air quality monitoring. IMPLICATIONS: Personal exposure monitoring at an urban hotspot indicated space and time variation in PM concentrations that is not captured by the fixed air quality monitoring networks. The short-term exposure to higher concentrations can have a significant impact on health that need to be considered for the health risk-based air quality management. The study emphasizes the need of hotspot-based monitoring complementing the already existing fixed air quality monitoring in urban areas. The personal exposure patterns at hotspots can provide additional insight into sustainable urban planning.

Assimilative capacity-based emission load management in a critically polluted industrial cluster.

In the present study, a modified approach was adopted to quantify the assimilative capacity (i.e., the maximum emission an area can take without violating the permissible pollutant standards) of a major industrial cluster (Manali, India) and to assess the effectiveness of adopted air pollution control measures at the region. Seasonal analysis of assimilative capacity was carried out corresponding to critical, high, medium, and low pollution levels to know the best and worst conditions for industrial operations. Bottom-up approach was employed to quantify sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter (aerodynamic diameter <10 µm; PM10) emissions at a fine spatial resolution of 500 × 500 m2 in Manali industrial cluster. AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model), an U.S. Environmental Protection Agency (EPA) regulatory model, was used for estimating assimilative capacity. Results indicated that 22.8 tonnes/day of SO2, 7.8 tonnes/day of NO2, and 7.1 tonnes/day of PM10 were emitted from the industries of Manali. The estimated assimilative capacities for SO2, NO2, and PM10 were found to be 16.05, 17.36, and 19.78 tonnes/day, respectively. It was observed that the current SO2 emissions were exceeding the estimated safe load by 6.7 tonnes/day, whereas PM10 and NO2 were within the safe limits. Seasonal analysis of assimilative capacity showed that post-monsoon had the lowest load-carrying capacity, followed by winter, summer, and monsoon seasons, and the allowable SO2 emissions during post-monsoon and winter seasons were found to be 35% and 26% lower, respectively, when compared with monsoon season. IMPLICATIONS: The authors present a modified approach for quantitative estimation of assimilative capacity of a critically polluted Indian industrial cluster. The authors developed a geo-coded fine-resolution PM10, NO2, and SO2 emission inventory for Manali industrial area and further quantitatively estimated its season-wise assimilative capacities corresponding to various pollution levels. This quantitative representation of assimilative capacity (in terms of emissions), when compared with routine qualitative representation, provides better data for quantifying carrying capacity of an area. This information helps policy makers and regulatory authorities to develop an effective mitigation plan for air pollution abatement.

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

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.

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 microm, 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 microg/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.

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 SO4²â» 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.

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management.

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November-March). The models have been developed for three 8-h groupings of 10 P.M. to 6 A.M., 6 A.M., to 2 P.M. and 2-10 P.M., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2-10 PM is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68-71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.