A Study on Effects of Weather, Vehicular Traffic and Other Sources of Particulate Air Pollution on t...

R. Gopalaswami

Journal of Environment Pollution and Human Health

A Study on Effects of Weather, Vehicular Traffic and Other Sources of Particulate Air Pollution on the City of Delhi for the Year 2015

R. Gopalaswami

Formerly Chairman of the Board and Managing Director, Bharat Dynamics Ltd., Hyderabad, India

Abstract

In the year 2014, WHO declared Delhi City as the worst polluted in the world. In December 2015, PM 2.5 levels in Delhi were at 295 microgram/m3 and PM10 levels were at 470 microgram/m3 resulting in Air Quality Index at a severely high 430-435. Air pollution is responsible for 10,000 to 30,000 deaths in Delhi every year. A critical convergence of public concern, policymaker attention, and academic contribution is now taking place to address this issue. Reducing emissions of PM2.5 not only has an immediate effect on air quality, but also mitigates near-term climate change and helps achieve food security. Data gathered over three year (2013-2015) on particulate air pollution PM 2.5 in Delhi reveals a systematic wave-like pattern each year. PM 2.5 rises and falls with rhythmic precision in winter and summer with a minimum range of values in the monsoon when the Delhi air is washed clean by rainfall. These waves of air pollution change are found to be nearly synchronous with a combination of weather factors, specifically ambient air temperature and dew point, modeled appropriately as a ‘weather index’. A near linear relationship is shown to exist between the air pollution (PM 2.5) level and the ‘weather index’ for the year 2015. So far, weather is not ascribed to be one of the primary reasons for unusually high particulate air pollution in Delhi, but merely a factor that influences air pollution. This study attempts to fill this gap Baseline Reference PM 2.5 measurements were obtained from published sources from a air quality monitoring sensor located in an elite, less densely populated locality near Delhi’s wooded Reserve Forest ridge, somewhat secluded from Delhi’s zones of high density traffic, industries and thermal power stations. Air quality measurements at this sensor location are routinely taken at hourly intervals throughout the year. The distance of the Reference Sensor from main sources of air pollution results in longer transport time from source to sensor, enabling the polluted air to be well modulated by weather. These weather-modulated measurements were compared with other reliable published data by the Government of India’s Central Pollution Monitoring Board from sensors at 10 other locations on the ground-level at kerb-sides of roads with high density vehicular traffic; and much closer to the commercialized and industrialized areas of the city. The street-located sensors record near instantaneous PM 2.5 measurements at the very starting place of air pollution so that the transport time interval required for deep modulation by weather may be presumed to be too short. Using the “weather index” property, theoretical values of air pollution, if there were no modulation by weather, were calculated from the Reference sensor values. These theoretical values are compared with aggregated experimental values of air pollution from sensor locations on the kerb-side of the roads so that were not modulated by weather. They are found to be in reasonably close (within 9%) agreement. Thus the validity of an exact relationship between PM 2.5 with the ‘weather factor’ determined by ambient air temperature and dew point is established ; and thereafter validated by measurements for seven different sources of air pollution at 10 locations in three seasons of year 2015: summer, monsoon and winter. Detailed pollution apportionment studies carried out by the Central Pollution Control Board and by the Indian Institute of Technology Kanpur are in reasonable agreement that vehicular traffic contributes to 12% of total particucale air pollution. Both studies report vey high contribution of dust (34 % and 51%). Apportionments of other components are also compared and show near agreement. The low proportion of contribution to air pollution by vehicular traffic in Delhi thus appears to explain why the outcome of the Delhi Government’s unique experiment to drastically reduce vehicular traffic by 50% for 15 days in a month, one month in winter and one in summer, did not appear to tangibly affect levels of particulate air pollution either in winter or summer seasons. It was clear that Delhi’s air pollution mitigation and control programmes need to focus all the year intensely on road-level pollution control. Measures are suggested focusing on resuspended road-dust and vehicular pollution. If the sum total of all the PM 2.5 components of particulate air pollution were made equal to 1.0 µg/m3, then the weather raises the PM-2.5 level to 7.0 µg/m3 in winter and 4.0 µg/m3 in summer due to weather. This weather component of air pollution can be addressed and mitigated in 10-15 years by environmental upgradation. Delhi has historically been a recipient of dust-laden winds and storms blowing across loose soil on 91,000 sq. Km of alluvial plains mostly now in Pakistan. A deeply wooded, broad forest belt around Delhi by altering local weather conditions in Delhi could help to significantly and permanently reduce chronic air pollution in the city due to dust. Detailed Emission Control Strategies and technologies for sustainable and massive reduction of air pollution in Delhi are presented which would enable a sustainable policy response be formulated and implemented.

Cite this article:

  • R. Gopalaswami. A Study on Effects of Weather, Vehicular Traffic and Other Sources of Particulate Air Pollution on the City of Delhi for the Year 2015. Journal of Environment Pollution and Human Health. Vol. 4, No. 2, 2016, pp 24-41. http://pubs.sciepub.com/jephh/4/2/1
  • Gopalaswami, R.. "A Study on Effects of Weather, Vehicular Traffic and Other Sources of Particulate Air Pollution on the City of Delhi for the Year 2015." Journal of Environment Pollution and Human Health 4.2 (2016): 24-41.
  • Gopalaswami, R. (2016). A Study on Effects of Weather, Vehicular Traffic and Other Sources of Particulate Air Pollution on the City of Delhi for the Year 2015. Journal of Environment Pollution and Human Health, 4(2), 24-41.
  • Gopalaswami, R.. "A Study on Effects of Weather, Vehicular Traffic and Other Sources of Particulate Air Pollution on the City of Delhi for the Year 2015." Journal of Environment Pollution and Human Health 4, no. 2 (2016): 24-41.

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At a glance: Figures

1. Introduction

The year 2015 was a landmark year for the capital city of India, New Delhi, when in December 2015 particulate air pollution levels recorded an alarming tenfold over the safety limit prescribed by the World Health Organisation (WHO), and four times higher than safety standards prescribed by the Central Pollution Control Board (CPCB) of India. One year earlier, the WHO had declared Delhi as the most polluted city of the world. Air pollution is responsible for 10,000 to 30,000 deaths annually in Delhi as it is the fifth leading cause of death in India, as reported by the Centre for Science and Environment (CSE) [1].

Experiments in Delhi on Large Scale Traffic Volume Reduction With nearly 9 million vehicles on the road, the Delhi Government in a trend setting initiative, decided to try a bold experiment reducing traffic volume by 50% on alternate days for 15 days from 1st. to 15th January 2016. The results were inconclusive. The experiment was repeated again from 15th to 30th April 2016 when ambient air temperatures increased substantially. The conclusion of a Study [2] published on 13 May 2016 analysing the results of these innovative experiments suggests that it is unlikely that large scale reduction of volume will have any insignificant impact on air pollution. Clearly there are many other factors other than vehicular traffic. This Study attempts to provide a more comprehensive picture to verify this conclusion.

International Perspective Air Pollution (PM2.5) levels in Delhi have been very high in the past as well. Its cyclical and seasonal variations over the period January 2013 to December-2014 were obtained from an international study comparing India (Delhi) and China (Beijing) [3] and tabled along with PM2.5 measurements for the period January 2015 to December 2015 obtained from actual measurements in the city of Delhi [4]. The 3-year longitudinal time-sequenced pattern of air pollution variations are shown in Figure 1.

Figure 1. [5] Comparison of Particulate Air Pollution PM 2.5 in Delhi and Beijing Over A Period of 3 Years 2013 -2015

It can be seen from Figure 1 that air pollution manifests in seasonal waves, peaking systematically to high, hazardous levels only in Delhi during the winter season. However, the average PM 2.5 levels in Delhi over the last 3 year period (125 micrograms/cu.metre) are about 32% higher than those recorded in Beijing in the same period (95 micrograms/cu.metre). This statistic of averages however gives little comfort in the real world. It is like telling a person who cannot swim that the average depth of the river Ganges at a particular location is 5 feet and hence he could cross the river safely!!

Health Hazard Air pollution generated by particulate matter is largely generated by dust and vehicular exhaust chemicals including solid carbon emissions that combine with it to form smaller and toxic particles. It is a deadly cocktail for human beings in any part of the world. It causes permanent DNA mutations, besides lung and heart diseases and cancer [6]. Cases with flu-like symptoms, including viral infections affecting the respiratory tracts and pneumonia, have reported an astounding jump of almost 150 per cent during this period. Nearly nine persons in 10 are sneezing, coughing or complaining about breathlessness. Respiratory illness cases have suddenly gone up from 10 a day before last week to 25 nowadays. The persisting smog and changes in the weather condition are posing a great health risk [7].

Mortality An early WHO study concluded that the impact of particulate matter on total non-trauma deaths in Delhi was smaller than the effects found in the US, but found that a death associated with air pollution in Delhi caused more life-years to be lost because these deaths were occurring at a younger age [8]. More than 5.5 million people worldwide die prematurely every year due to household and outdoor air pollution. It is reported that India and China together account for 55 percent of these deaths. In 2012 about 1.6 million people died of air pollution in China and 1.4 million died in India in 2013; so low average values of air pollution are not a matter of comfort either in Beijing or in Delhi [9].

A leading national newspaper, the Economic Times, brings out [9] that more than 85 per cent of the world's population lives in areas that exceed the World Health Organization’s 'safe level' of air pollution. It indicates that according to new research more than 5.5 million people die prematurely each year due to air pollution with over half of those deaths occurring in China and India, two of the world's fastest-growing economies.

Effect of Weather on Air Pollution Scientists say in a general way that the level of pollution is influenced by weather conditions. Specifically for Delhi, it is reported [10] that in winter, cool air causes “inversions” that stagnate the air and trap pollution close to the ground. Air flow patterns from Afghanistan and Pakistan pick up emissions as they move over the densely urbanized regions of Punjab and Haryana. Pre-monsoon dust storms also contribute to air pollution in the region. Northwest winds [11] and sunshine however improve Delhi air quality.

On Wednesday, 23rd December 2015, PM 2.5 levels in Delhi were at 295 microgram/m3 and PM10 levels were at 470 microgram/m3 resulting in Air Quality Index at a severely high 430-435. Nikita Mehta [12] from the System of Air Quality and Weather Forecasting And Research organization (SAFAR) in Ministry of Earth Sciences of Government of India brings out being “…puzzled over the jump in air pollution seen from 21 to 23 December 2015 and said it is unusual and cannot be explained by meteorology….” thus contradicting the findings of the other (private) organization [10] that cool air causes “inversions” that stagnate the air and trap pollution close to the ground.

Sources and Apportionment of Particulate Air Pollution Apart from vehicular traffic and weather conditions, it is well known that several factors enhance dangerous particulate air pollution in Delhi City, such as emissions from a number of large coal based thermal power stations nearby; a large number of industries in and around the city and dust from across the plains in the capital city region. There are also lesser known sources like dust from construction activities, use of diesel generator sets during power outages, coal based “tandoors” for cooking roti’s, from hotels & restaurants, municipal waste disposal and biomass burning. Farmers burn the straw in their fields and pull this pollution into Delhi. Other meteorological conditions like wind speed and direction are also well known factors, though an exact relationship of weather to level of air pollution in various seasons of the year has not been investigated.

Rohini Pande writing from Harvard [13] says “Without a better knowledge of the portion contributed by different sources, any policy response, no matter how bold, will be a little like a blind attempt to pin the tail on the donkey….. at a time like this (and here she refers to the concern for peaking air pollution in December 2015 and January 2016 ) – (there is) a critical convergence of public concern, policymaker attention, and academic contribution–it’s worth taking stock of what we know now and how we came to know it. That clarity will make it easier to guide the discussion toward a policy response that will stick.”

Comprehensive apportionment studies have been done by the Government of India’s Central Pollution Control Board (CPCB). A measurement data-packed 698-page Study on Air Quality Monitoring was carried out by CPCB [14] at TEN key locations in Delhi. Ambient Air Quality Monitoring stations were very carefully chosen to represent the whole of Delhi and its varied air pollution characteristics. Seven sources of air pollution were identified and seasonal variations of air pollution were measured.

U.N. Finding A UN Report of 2014 [15] brought out that air pollution was world’s worst environmental health risk. It brought out that reducing emissions of PM2.5 and ground-level ozone not only has an immediate effect on air quality, but also mitigates near-term climate change and helps achieve food security. These are important human goals.

Purpose of this Study The purpose of this Study is therefore an attempt to close this gap of understanding the specific weather factors are responsible for the high level of air pollution in winter months; and to establish a clear relationship between weather and air pollution over the City of Delhi.

In this process, an attempt is made to obtain a better understanding of multiple factors influencing the magnitude of air pollution phenomena. An extensive scope of antipollution measures over a broad front is needed and suggested for mitigating the entire range of pollution-enhancing factor. All measures suggested are targeted at the most dangerous pollutant PM2.5 as the means to have an immediate effect on air quality in Delhi.

2. Literature Review

Models for assessing air pollution in cities have been have been carried out by scholars and researchers in India for several major state capital cities in India, like Delhi, Mumbai, Calcutta Chennai, Ahmedabad, and even in whole Districts like Maharashtra.

Air Pollution and Health Much of research in India on air pollution is focussed on health aspects. In 2014 Dholakia HH et. al. in the first multi-city study in India [16], assess heterogeneity of air pollution impacts and possible modification due to temperature in Indian cities that are spread across climatic regions and topographies. The paper brings out that while Indian cities are among the most polluted globally, yet assessments of short term mortality impacts due to pollution have been limited. Furthermore, studies examining temperature – pollution interactions on mortality are largely absent. Addressing this gap remains important in providing research evidence to better link health outcomes and air quality standards for India.

Empirical Studies on Vehicular Pollution Pioneering empirical research in vehicular air pollution in 2001, Sengupta [17] who was the Member Secretary of India’s Central Pollution Control Board carried out and made a presentation of comprehensive data on vehicular air pollution in the city of Delhi at an ESCAP/UN (DESA) in Bangkok. This data served as an empirical baseline reference for further studies in this area in Delhi. It is only recently that more complex studies, modeling and analysis have been carried out in major urban centres as the problem of air pollution due to increase in vehicular traffic became acute in India.

Empirical and Analytical Studies While Sengupta focused on the vehicular aspect, Biswas et al [18] carried out detailed analysis of ambient air quality conditions over Delhi, from 2004 to 2009. They found that the diurnal averages of criteria pollutants (NO2, SO2, CO, PM2.5 and PM10) reveal that vehicular emissions strongly influence temporal variations of these pollutants. Their seasonal analysis of criteria pollutants reveals relatively higher concentrations in winter because of limited pollutant dispersion and lower concentrations during the monsoon period (rainy season) that is in agreement with this Study. The causes of “limited dispersion” were not analysed further.

Nesamani [19] from the California Institute of Technology in a Study of automobile emission and control strategies for the city of Chennai noted in 2009 that most cities in India have exceeded the National Ambient Air Quality (NAAQ) standards. Particulate matter (PM) is a major concern in Indian cities and 60 out of 62 metropolitan cities have exceeded World Health Organization (WHO) standards. About 2.5 million premature deaths are caused annually due to PM exposure (Pandey et al., 2006). His study estimated the emissions (running and start) from on-road vehicles in Chennai using the International Vehicle Emission (IVE) model is used to estimate accurate emission inventory in this study. GPS was used to collect driving patterns. He concluded that aggressive efforts are required to reduce vehicular emissions and several operational strategies were recommended were made including incentives to encourage faster penetration of advanced technologies such as hybrid vehicles, BOV, and fuel cells

Modeling, Analysis and Empirical Studies Kar and Damodaran [20] examined models for assessing air pollution in cities with focus on the city of Ahmedabad, the Capital of Gujarat State where they carried out a most comprehensive and sophisticated analysis, verified against measurements. For air quality modeling, the Gaussian plume model was been extensively used to analytically solve the steady and unsteady transport equation including the effects of particle deposition and settling. He explored the adaptation of a Lagrangian Branched Atmospheric Trajectory approach for computing ground level concentrations of PM10 (suspended Particulate Matter of aerodynamic diameter of less than10 micro meter), PM2.5 emitted from different air pollution sources in and around the city of Ahmedabad, India. With a transport time step of 6 hrs, the model included necessary meteorological data which couple with multi-pollutant emission grid. The results obtained from the model are found to be very close to the measured PM10 data (2009-2011) from Gujarat Pollution Control Board.

Standards This paper analyses air pollution in Delhi against the standards prescribed by the Central Pollution Control Board, Government of India and the World Health Organization (WHO). The standards referred to are shown in Table 1.

Table 1. Comparison of CPCB (India) and WHO Standards for Particulate Air Pollution

Previous Studies on Air Pollution Sources and Apportionments Recently [23] (January 2016), Pande et al state “…..that over the past 10 years and excluding a just completed IIT Kanpur Study, there are 15 source apportionment studies that sought to pinpoint the sources of emissions and their respective contributions to Delhi’s overall air pollution. Ten are based on direct sampling; the other five rely on secondary data. While the main sources identified are similar across studies, the relative weights placed on different sources by these studies vary dramatically. This underscores both the difficulty of conducting them and the wide range in quality of the studies currently available…….”

The authors bring out that there are two ways of conducting source apportionment studies: direct sampling based on chemical analysis, and secondary data analysis based on monitoring data. International best practice is to rely on receptor-based studies, but where budgetary constraints inhibit adequate sampling, analysis using secondary data may dominate. The “state-of-the art” in India thus far is limited to direct sampling analysis ( as in the case of monitoring of air quality index my multiple agencies) and secondary data analysis based on monitoring data. Receptor-based studies like are extremely complex and resource consuming, because are not only concerned with source contributions but also with the influence of transport, transformation, and deposition processes on speciated atmospheric concentrations at receptor locations [24].

The authors conclude “….Once the low-hanging fruit is picked, policymakers will need increasingly detailed information on location, source, and type of air pollution to design a smart policy response. Now is the time to get the information systems in place that will inform the next decade of environmental policy in the city, and beyond…”. This Study relies on secondary data analysis, the “low hanging fruit” based on monitoring data thus adds yet another set of values and perspectives (like effect of climate on air pollution.

3. Materials and Methods

The materials and methods used for the study on air pollution and effect of weather is now presented separately from the materials and methods for apportionment among sources of air pollution in Delhi

3.1. Materials and Methods for Analysis of Air Pollution and Weather Effects

The focus of this paper is on particulate air pollution (PM 2.5) on air quality in Delhi. PM 2.5 is a standard recognized by the U.S. Environmental Protection Agency. Data needs to be collected from several monitoring stations to arrive at a general picture for the entire city as pollution is location specific. While the sensor in Chanakyapuri area was used in this Study as the Baseline Reference sensor to generate an annual data base, the other sensors 10 are operated by the Central Pollution Control Board and their individual specifications are not known. One of the CPCB sensors is located within 5 kilometers of the Reference Sensor. Remaining 9 sensors are located at distances from 10 to 21 kilometers (obtained from Google Maps) from the Reference Sensor

Specifications of PM 2.5 Measuring Equipment for Annual PM 2.5 Data Base [25] PM 2.5 are a standard recognized by the U.S. Environmental Protection Agency. The American Embassy has five Air Quality Monitoring (AQM) stations consisting of BAM-1020 housed in the environmentally controlled weather proof unit, supplied with 230/400 V/ 3 phase/50 Hz power supply. The unit monitors PM 2.5 particulate concentration by Beta attenuation method in the ambient air sample on hourly basis. The range of BAM 1020 is currently limited to read up to 985 microgram and it is proposed to upgrade it to read up to 2000 microgram. The system currently uses Envitech data acquisition software and Windows 7 (professional)

Effect of Site Location of Sensors Data from a single monitoring station cannot be applied to an entire city. Therefore, air quality data collected at the U.S. Embassy differs from other monitors located in the same cities. To elaborate this important point, typical variations of PM 2.5, ambient air temperature, relative humidity and dew point in different locations in Delhi but at the same instant in time are shown in Table 2, obtained from http://aqicn.org /city/india/ new-delhi/us-embassy/.

Table 2. Air Pollution and Weather Factors in Different Localities of Delhi at a Given Instant

Figure 2. Map of Delhi Showing Approximate Relative Locations of Reference (Chanakyapuri) Air Quality Monitoring Station in US Embassy School, and 10 other Air Quality Monitoring Stations Set up by Central Pollution Control Board (CPCB) from their Study Report from Fig E.2: Locations of Ambient Air Quality Monitoring Stations http://cpcb.nic.in/Delhi.pdf (Map reproduced from http://www.mapsofdelhi.com/delji-map.html WITH DUE PERMISSION from MapsofIndia.com. vide Email harneet@infobase.in dated 16 May 2016)

Effect of Sensor Location on Recorded Ambient Air Temperature and Dew Point The site sensor for this study was the Chanakyapuri Locality (Figure 2) [26] within less than a kilometer of Delhi’s Southern Ridge Forest and the Pusa Forest. It thus be can be seen from Table 2 that even in mid-summer, this location hosts the greatest relative humidity and also the lowest dew point. This is because Chanakyapuri is a large, open area for exclusive housing of the Embassies of a large number of nations of the world with probably the least population density in the city. Nevertheless here also pollution levels are high and unhealthy due to transport of particulate matter over the whole area of Delhi.

Effect of Sensor Location on Modulation of Air Pollution Measurement by Weather and Winds Baseline Reference PM 2.5 measurements were obtained from published sources from a sensor located in a school in Chanakyapuri, an elite (reserved exclusively for Embassy from other nations), relatively sparsely populated locality near Delhi’s wooded Reserve Forest ridge, somewhat secluded from Delhi’s zones of high density traffic, industries and thermal power stations. Air quality measurements at this sensor location are routinely taken at hourly intervals throughout the year. The distance of the Reference Sensor from main sources of air pollution results in longer transport time from source to sensor, enabling the polluted air to be well modulated by weather.

These weather-modulated measurements were compared with other published data from CPCB sensors at 10 other locations on the ground-level at kerb-sides of roads with high density vehicular traffic; and much closer to the commercialized, industrialized areas and highly populated areas of the city. The street-located sensors record instantaneous PM 2.5 measurements at the very starting place of air pollution so that the transport time interval required for deep modulation by weather may be presumed to be too short.

Site and Data Availability Only US Embassy New Delhi data were available with longitudinal information. PM 2.5 measurements from this sensor are taken hourly on days of the whole year. Days where data were not reliable are indicated as “Invalid”. The web site gives historical data only for one past year, and is published monthly while daily readings are available at http:// aqicn.org/ city /india/new-delhi/us-embassy.

Reliability of Measurements Accuracy of this data is by inferential methods and not by cross check from an alternate source or experimental verification in the same location at the same time (an example of inferential accuracy was on 11 November 2015 the PM 2.5 level went sharply up to over 1000 µg/m3 because it was “Diwali” day, a national social festival when thousands of tonnes of firecrackers are burnt; and in May 2015, a heat wave resulted in a small but tangible increase in PM 2.5).

Standards for Air Pollution and Related Health Standards The World Health Organization, the US EPA and the Government of India have set standards of air pollution that are considered safe or hazardous to health. All however are based on PM 2.5. It is not the intention to compare these standards except to point out that the standards in India that are known to be lower than WHO and EPA. The Ministry of Earth Sciences, Government of India and its monitoring and analysis organization SAFAR have (in 2010) issued the following standards, shown in Figure 3 that indicates Air Quality Index in sub-indices grouped in FIVE Good, Moderate, Poor, Very Poor and Severe. Each sub-group has a Health Advisory and Health Statement.

Figure 3. Standard Chart for Assigning Health Hazards to Particulate Air Pollution PM 2.5 Issued by SAFAR, Ministry of Earth Sciences, Government of India

The standard is based on Air Quality Index (AQI) and not PM 2.5 directly. An algorithm converts raw PM 2.5 readings into an air quality index (AQI) value that can help inform health-related decisions. AQI Calculator is available at https://airnow.gov/index.cfm?action=resources.conc_aqi_calc The relationship between PM 2.5 and AQI is estimated using this algorithm as related to SAFAR 2010 standard is shown in Figure 4.

Limitations of Materials and Methods The data gathered from which the findings, analysis and conclusions follow have the following limitations

1. The study, analysis, and modeling are based entirely on published historical data of particulate air pollution PM 2.5 from a single source and a single location in Delhi for a single period, namely year 2015. Accuracy of this data is by inferential methods and not by cross check from an alternate published source or experimental verification in the same location at the same time. Experimental set up/technique used for generating this data is not stated by the source authority but reported as meeting US EPA standards.

2. Source of data from some published sources comparing longitudinal variation of PM 2.5 in Beijing and Delhi in years 2013 and 2015 though reliable have presented the data in Chart and not Table form, from which numerical data is extracted for this Study. Such data was not made available in Table form and hence may be deemed only as approximate estimates and not actual reported measurements. The exact location(s), date(s), times(s) and weather conditions under which the data from which the chart was prepared and published are not reported.

3. Source of data from some published sources on apportionment of particulate air pollution from multiple sources in Delhi though reliable have presented data in Pie Chart form with data labels which are extracted and placed in Table form for analysis.

4. This research Study reports an empirical relationship between certain weather parameters and particulate air pollution PM 2.5 in one location namely Chanakyapuri in New Delhi. This weather-air pollution relationship is valid for the specific location mentioned for the period mentioned. It is not relevant for any other city.

3.2. Materials and Methods for Apportionment of Air Pollution Sources in Delhi

Awareness of the serious dimensions of air pollution in Delhi has resulted in a very large number of published articles on the likely sources of such pollution.

Early General Apportionment Studies In 2013 Rizwan et al [8] quoting a report by the Ministry of Environment and Forests, India, in 1997 reviewed the environmental situation in Delhi over concerns of deteriorating conditions. Air pollution was one of the areas of concern identified in this study. It was estimated that about 3000 metric tons of air pollutants were emitted every day in Delhi, with a major contribution from vehicular pollution (67%), followed by coal-based thermal power plants (12%). There was a rising trend from 1989 to 1997 as monitored by the Central Pollution Control Board (CPCB). Delhi has the highest cluster of small-scale industries in India that contribute to 12% of air pollutants along with other industrial units. The early (1997) data on apportionment of pollution sources are set out in Table 3.

Central Pollution Control Board (CPCB) Study [26] on Pollution Source Apportionment in Delhi: The Ministry of Environment and Forests, India, in 1997 framework includes coal based thermal power stations located around the perimeter of the city. The other components are vehicular, area and point based sources had not been specified but are now thoroughly studied and analyzed by a very comprehensive 698-page Study on Air Quality Monitoring was carried out by CPCB [26] in the period 2009 -10 (date of Study not mentioned in Report) at TEN key locations in Delhi. Ambient Air Quality Monitoring stations were chosen to represent the whole of Delhi and its varied characteristics. The source categories and types of air pollution is summarized in the Table 4 [26].

The composition of these pollution sources is brought out fully in the CPCB Study. The source characteristics are not limited to overall chemical and physical components but to very specific chemical fractions of different metals or ions. Based on the knowledge of signature of sources, they are related to these different factor loadings and their quantified values are considered as the proportionate source contribution to ambient air pollution. In this method of analysis of source contribution, only the chemical speciation of PM in the ambient air is required. On the contrary, CMB approach requires knowledge of chemical speciation of PM in both ambient air and number of possible sources. These sources need not be well defined geometric shapes as in the stacks; instead they may be PM from wind blown dust, resuspended road dust, waste burning emissions, vehicular emissions etc.

CPCB Study states that gaseous criteria pollutants like SO2 and NOx cannot be studied by their methodology (CMB) as once they are mixed in the ambient air, they cannot be separated due to lack of any source specific signature. In the case of source apportionment study at, Delhi, a survey was carried out to determine possible sources of pollution. Thereafter PM10 and PM2.5 samples were collected from the source and analyzed for various chemical species. Ambient air particulate matter i.e. PM10 and PM2.5 were collected during the three seasons and then analyzed for the same chemical species as those present in source PM. FA tool is applied to PM10 primarily as a screening tool for identifying the possible sources of pollution. These sources were then used in balancing the mass with those of ambient air in CMB methodology.

Hence the CPCB Study pertains only to Particulate Air Pollution and does not include gaseous air pollution. While the earlier on Air Pollution Delhi White Paper brought out by Ministry of Environment and Forests, separates out coal based thermal power stations from other sources, the more detailed CPCB Study includes power stations in the category of “Industries”.

Table 4. Source Categories and Types of Sources of Air Pollution in Delhi

Methodology of Source & Apportionment of CPCB Study The Study objectives (of ambient air quality status, sources and emission loads) were achieved through monitoring of air pollutants at 10 locations using various instruments and multiple analyses. The methodology of the CPCB study was divided into four parts namely

1. Ambient air quality monitoring,

2. Sources emission inventory,

3. Source apportionment analysis and finally

4. Delineating an urban air quality management plan based on the data collected during the study.

Choice of CPCB Study Locations: These locations were selected on the basis of land use and activity profile. All the six kerb sites are highly influenced by vehicles such as Mayapuri, Anand Vihar and Loni Road. These are different than sites with very high traffic density such as Dhaula Kuan, ISBT and Ashram Chowk. Pitampura is a residential area, whereas Naraina is a mixed use residential-industrial zone and SSI-GTK is an industrial site. Prahaladpur was taken as reference site as it has least activity compared to many other locations in Delhi. Air Monitoring was carried out using varied instruments and all attributes were analyzed using standards methodologies. In the initial stages of the study design, wide spread traffic activities and dense road network were under focus. The final monitoring network of Delhi represented kerb side locations under different land-use types. Six stations were selected near road side, which are referred as kerb side stations with variable situation and location to represent the overall city traffic locations. One station was established in the industrial area to represent the industrial sources contribution. Another two stations were established in mixed and residential areas to understand the characteristic of such areas. All these nine stations were located within the core area of known sources of pollution within the city. However, to facilitate comparison of pollution from these sources with background levels, a station was also located at the outskirts of Delhi. Portacabins were installed at the monitoring sites to house instruments and other necessary equipments.

This CPCB Study confirmed that the seasonal variation is significant and varies from site to site. Seasonal variation indicates that values are normally higher in winter and post monsoon (as this sampling is close to winter months) compared to summer. This fact is also corroborated with meteorology data collected during the study as also from IMD. Large amount of biomass burning is prevalent all across Delhi and adjoining areas. It was seen that seen that for the two criteria pollutants (SPM, RSPM) the monitored values exceed the regulatory limit on most of the days in all seasons and the background dust contribution dominates the mass.

Methodology for Apportionment by the Central Pollution Control Board (CPCB) A typical presentation of the data gathered in this Study for the locality called “Ashram Chowk” reported to have very high vehicular traffic density is shown in Figure 5.

Figure 5. Typical Data Presentation of Air Pollution Apportionment in “Ashram Chowk”, Delhi Extracted from CPCB Report [26] Figure 4 Source contribution estimates using CMB mode

Based on season and locality-wise apportionment, data for all 10 locations have been extracted from the CPCB Study and placed conveniently in Table 5 for purposes of analysis.

Table 5. (Data Extracted From Pie Charts in Figures 4.1 to 4.5 in CPCB Study Report) Apportionment of Air Pollution in Delhi by Locality, Source and Season (Numbers are in %age Apportioned; W =winter/ M = Post-Monsoon / S = Summer)

4. Analysis of Data on Air Pollution, Weather & Apportionment Factors

Data extracted from Sensors at Chanakyapuri location in regard to air pollution and weather and from sensors at 10 different locations are analyses to establish a relationship between air pollution, weather and apportionment of air pollution values to 10 different locations as obtained from the CPCB Study.

4.1. Air Pollution Data from New Delhi (Chanakyapuri) Sensors

A typical Data Sheet for a given time period in Year 2015 is shown in Appendix “A”. The entire data from the website was analysed on Excel spread sheet. Table 6 presents month-wise average, maximum, minimum values of PM 2.5 data is presented as diurnal variation at hourly intervals from January 2015 to December 2015.

Table 6. Particulate Air Pollution in Delhi in Year 2015 (Baseline Data from PM 2.5 Sensor Site Location: Chanakyapuri )

Data Analysis: Long Term (3-year) Longitudinal Study on Air Pollution in India and China A similar international longitudinal study on air pollution PM2.5 is reported from the U.K. covering the capital cities of India and China, namely, Delhi and Beijing, for the period 2013 and 2014. These results have been shown in Table 7 along with the data from Delhi for year 2015. In Delhi the cyclical variation of PM 2.5 has a minimum in rainy season (July-August) and maximum in winter (November-December). It may be noted that rainy season data in Delhi for year 2015 is obtained from Chanakyapuri among the more “green” the locations in Delhi as discussed earlier. The Sensor site locations in Delhi and Beijing are not specified for 2013 and 2014 in the reference but appears was for localities in Delhi with far higher air pollution than Chanakyapuri. The comparative trends in terms of seasonal rise and fall of air pollution are nevertheless discernable.

Table 7. Monthly Average Particulate Air Pollution (PM 2.5) over a 3-year period in Delhi and Beijing

The Air Pollution – Weather Relationship in Delhi It can be seen from Figure 6 that air pollution peaks systematically to high, hazardous levels only in Delhi during the winter season, November December and January even over a 3 year period. It can be seen from Figure 6 that air pollution peaks systematically to high, hazardous levels during the winter months, November, December and January. However, till date, no explicit relationship has been scientifically established between air pollution and weather factors. Until this is done one cannot take steps to mitigate air pollution in Delhi.

Figure 6. Seasonal Variation of Particulate Air Pollution in Delhi

While some experts believe that in winter, cool air causes “inversions” that stagnate the air and trap pollution close to the ground, others aver that the jump in air pollution seen from 21 to 23 December 2015 is unusual and cannot be explained by meteorology. The long–cycle international study when tabled along with this study establishes that such jumps are NOT unusual as reported and indeed have been happening every year.

Diurnal Variability of Air Pollution in Delhi Table 6 giving month-wise diurnal average, maximum and minimum values of PM 2.5 for Delhi are plotted on a graph and placed in Figure 7 An important observation here is that there are cyclical seasonal variations of air pollution taking place in Delhi perhaps since a long time and not noticed until the exploding urban and vehicular population has resulted in a most hazardous health situation. These peaks of pollution are thus far just not understood, and will no longer be seen as unusual for the city of Delhi once the weather factors responsible for the peaks are better understood by analysis.

Figure 7. Peak, Average and Minimum Monthly Average of Particulate Air Pollution in Delhi as Measured by the Reference Sensor (Data from Table 6)

It has already been observed that cool air causes “inversions” that stagnate the air and trap pollution close to the ground. It is hypothesized in this Study that two factors control this phenomenon, ambient air temperature (t) and dew point (tD). The colder the air, the denser it is. Thus dense, moist air “trap”, or bind together, carbon, dust and tyre-rubber that constitute particles particulate air pollution and this mixture sinks to the ground. These two physical parameters ambient air temperature (t) and dew point (tD) are tied together mathematically in a factor called Relative Humidity (RH).

RH and Dew Point Relative Humidity is the ratio (expressed as a percentage) of the amount of moisture actually in the air to the maximum amount that can be present at that temperature. Dew Point is the temperature at which a given sample of air at a given ambient air temperature will have a relative humidity of 100 percent; hence, the saturation temperature. All three -- relative humidity, temperature and dew point -- are bound together in the mathematical relationship [28].

td = t, the dry-bulb temperature, when RH =100%. This relationship is expressed graphically in Figure 8.

Figure 8. Relationship between Dew Point, Ambient Air Temperature and Relative Humidity [31]

For a given Dew Point, the RH varies as a function of ambient air temperature in the manner shown. Given any two factors the third is directly seen and calculated.

Concepts of “Weather Index” (WI) Two “weather factors” were conceived as being able to rationalize the effect of weather on air pollution in cold wet air. The first is Relative Humidity (RH) which is the amount of moisture present in the air to what the air can hold at that temperature. (It is also the ratio of the partial pressure of water vapour to the equilibrium vapour pressure of water at the same temperature.) This does not involve dew point, which is an essential factor in air pollution. Hence a new factor, called “Weather Index” (WI) was postulated where

The values of WI and RH were calculated for the year 2015 for each month January to December. The data and calculations are tabulated in Table 8. In India, and in Delhi, the months July and August are monsoon periods with heavy rainfall. The rain washes the air pollution down every day. Hence as will be observed in Figures…, air pollution is least in July-August months. Hence all values of WI and RH are “normalized” taking the value of WI and RH in July = 1.

Table 8. Values of Weather Index and Relative Humidity in Delhi for Different Months in Year 2015

From this data, the annual variations of Air Pollution Factor PM 2.5 and WI factor can be visualized and compared in graphical form in Figure 9 and Figure 10.

A very clear pattern relating air pollution to air moisture-density factor emerges. It shows that AMD increases by a factor of 7 in winter months as compared to baseline value in July during the rainy season, PM 2.5 increases by a factor of 7.5. A similar correlation was attempted from Table 8 using relative humidity (RH) for each month instead of WI. This is shown in Figure 11.

Figure 9. Seasonal Variation of Air Pollution (PM 2.5) in Delhi Year 2015

Selection of Weather Factor for Investigations on Air Pollution Figure 11 as expected shows that RH is a maximum in July/August, the rainy season. This trend is not useful as much as the Weather Index (WI) that shows a distinct increase in winter months in Delhi as compared to the rainy season when air pollution is expected to be the least throughout the year; and is also synchronized exactly with variations of PM 2.5.

Correlation between PM 2.5 and WI Hence for further analysis of PM 2.5, the AMD was chosen as an appropriate factor to visualize the effect of weather on air pollution. The values of monthly average PM 2.5 from January to December 2015 for year 2015 were plotted along with AMD (weather index) factor for the same months. The result is shown in Figure 12.

Figure 10. Seasonal Variation of Weather Index WI =1/(t*tD)] in Delhi Year 2015
Figure 11. Seasonal Variation of Relative Humidity in Delhi Year 2015

The pattern of cyclical variations in PM 2.5 with WI is seen in Figure 12 Seasonal (monthly) time factor is now removed as a variable, the values of WI vs. PM 2.5 are plotted and are shown in Figure 13.

A linear relationship is obtained. Thus the relationship between air pollution in Delhi for year 2015 is very clearly established. However the limitation as explained earlier is that the data and analysis is unique to Chanakyapuri locality only. This weather characteristic is to be determined for every locality in Delhi because as is seen from Table 2 the Ambient Temperatures and Dew Points can vary from one locality to another.

Extensions of Weather factor Analyses Further, since annual rhythms of air pollution (2013 to 2015) bear great deal of similarity, it can be expected that this WI factor can be used as an analytical tool for the city of Delhi in future as well and studied for longer periods to see the effects of global climate change on air pollution in Delhi. Similar weather-air pollution relationships need to be developed for all polluted cities in India and cases compared to see whether this is a universal pattern.

Figure 13. Relationship between Weather Index and Particulate Air Pollution in Delhi Year 2015 at Reference Sensor Location
Figure 14. Comparing the Seasonal Patterns of Air Pollution (PM 2.5) With the Effect of Weather Estimated by the Weather Index (WI)

Seasonal Variations of Weather and Air Pollution on Health The World Health Organization, the US EPA and the Government of India have set standards of air pollution that these authorities in their wisdom consider safe or hazardous to health. All however are based on PM 2.5. It is not the intention to compare these standards except to point out that the standards in India are seen to be lower than WHO and EPA. The Ministry of Earth Sciences, Government of India and its monitoring and analysis organization SAFAR have (in 2010) issued the following standards, shown in Table that indicates Air Quality Index in sub-indices grouped in FIVE Good, Moderate, Poor, Very Poor and Severe. Each sub-group has a Health Advisory and Health Statement. These have been discussed in the section on Materials and Methods. Based on the relationship between air pollution (PM 2.5) and weather (WI factor) an integrated perspective regarding effects of seasonal variations weather and air pollution, impact on health of citizens in accordance with Government of India (SAFAR 2010) standard is set out in Figure 14.

Table 9. Impact on Particulate Air Pollution PM 2.5 Without Weather Amplification (WI) Effects

Theoretical Value of Air Pollution in Delhi without Weather Amplification Effects Table 9 sets out what would be air pollution values in Delhi without weather modulation effect. By applying the AMD factor on the monthly average of PM 2.5 values; the effect of weather is mathematically removed from the measured values on PM 2.5. This is done in Table 9.

From this Table it can be inferred that weather is the primary factor standing between the Delhi becoming almost pollution free (i.e. theoretically PM 2.5 would never exceeding say 5O) that would make it among the least polluted cities of the world.

Actual Effect of Weather Conditions on Air Pollution in Delhi The weather conditions are such that in summer,

• In summer, 1 µg/m3 of air pollution generated by human activity will be amplified to a maximum of about 4 µg/m3.

• In the monsoon season, rains wash down all particulate air pollution. Hence the effect of weather is not appreciably discernable.

• In winter, 1 µg/m3 of air pollution generated by human activity will be amplified to a maximum of about 7 µg/m3

4.3. Comparison of Results of CPCB and IIT (Kanpur) Studies and Apportionment Analysis

The Delhi Average apportionment for each Source and each Season is extracted from Table 9 and shown in Table 10.

Table 10. Comparison of Theoretical Values of Air Pollution without Amplification by Weather of This Study With Seasonal Average Apportionment of Particulate Air Pollution Aggregated from 10 Localities in Delhi City as Reported in CPCB Study

The Apportionment of air pollution from Table 10 is plotted as a graph (Figure 15) to visually observe the trend through the seasons for the seven different components of air pollution in Delhi.

Figure 15. Annual Seasonal Variations of PM 2.5 Measured in Chanakyapuri with Appropriations of All Air Pollution Sources Averaged From 10 Locations In Delhi (Measured as reported by CPCB)

Analysis of IIT (Kanpur) Report on Appropriations [29] The Delhi Government has contracted the Indian Institute of Technology (Kanpur) to carry out an apportionment Study. The original Report yet unpublished. Hence the locations and measurement techniques deployed are not known. The experimental techniques used are also not known. But the data quoted in this Study are consistent from several news articles that brought out the important contents of the Report. The appropriation due to coal burning thermal power stations is also not reported. The Study has estimated the total PM10 emission load in the city at 143 tonnes per day and listed the top contributor as road dust (56%) and other components. The PM2.5 load at 59t/d, and the top contributors are road dust (38%) and other components. as brought out. From this published data the following estimates of total appropriations are made and Tabulated in Table 11.

Table 11. Comparison of Results from the Studies of the Central Pollution Control Board (2010) and Indian Institute of Kanpur (2015)

Comparison the two Appropriation Studies A comparison is made between appropriations made by the earlier CPCB Study and this recent IIT (Kanpur) Study in this Table. It can be seen that excepting for the dust, domestic and construction components there is otherwise reasonable agreement between the two Studies. In regard to vehicular, industrial, diesel generating (DG) sets and opens burning. Since the CPCB study brings out that dust, domestic and construction vary a lot with the season, it may be presumed that the difference may be due to differing seasons when the measurements of dust were made by the two Studies.

Appropriate emission control strategies may now be readily formulated on the basis of these findings.

5. Recommended Emission Control Strategies

Strategies for reduction and ultimate elimination of air pollution in Delhi are summarized in Table 12. Several are already recommended by the IIT(K) Study and are brought out here for sake of completeness.

6. Discussions & Conclusions

So far, weather is not ascribed to be one of the primary reasons for unusually high particulate air pollution in Delhi, but merely a factor that influences air pollution. This study attempts to fill this gap.

Impact of Local Weather Conditions The attempted approach of determining an exact relationship between particulate air pollution and weather conditions is a novel feature of the Study. Air pollution data having been sourced by sensors at 11 locations, the Reference Sensor being fortuitously located in a sparsely populated locality far away from heavy traffic and the 10 others located directly on the kerb-sides at ground level of roads in the heart of densely populated areas with high traffic density, industries etc has enabled a representative study of the whole of Delhi.

It has been established both mathematically by the weather analysis, and empirically validated by the apportionment analysis that the ‘weather index’ approach to analysis is unique to the city of Delhi and not universal to all urban centres. Since nearly 78 cities of India are reported to be highly polluted, this approach may be attempted for all cities and differences in effect of ‘weather index’ studied. It can be inferred that the weather conditions in Delhi actually amplify air pollution from all the recognized sources. In December and January weather amplifies air pollution by a factor of 6.0 to 7.0; in summer by a factor of about 4. If the sum total of all the PM 2.5 components of particulate air pollution were made equal to 1.0 µg/m3, then the weather raises the PM-2.5 level to 7.0 µg/m3 in winter and 4.0 µg/m3 in summer due to weather. This weather component of air pollution can be addressed and mitigated in 10-15 years by environmental upgradation. Resuspended Road Dust has been seen to be the major contributor to particulate air pollution in Delhi (34% to 51% ) followed among others by vehicular pollution (12%) as set out in Table 11.

The low proportion of contribution to air pollution by vehicular traffic in Delhi thus appears to explain the outcome of Delhi Government’s unique experiment to drastically reduce vehicular traffic by 50% for 15 days in a month, one month in winter and one in summer, that did not appear to tangibly affect levels of particulate air pollution either in winter or summer seasons; though of course contributing to the temporary mitigation of global warming not to speak of freedom from heavy traffic jams. It was clear that Delhi’s air pollution mitigation and control programmes need to focus all the year on intense road-level attention.

Measures are needed to mitigate re-suspended road dust and vehicular pollution thus addressing 46% to 63% of the air pollution in Delhi. A separate and equally well organized drive is needed to address the remaining 37% - 54 % of air pollution from all the other sources.

Striking at the Roots: Deep Forestation for Changing the Weather over Delhi One cannot change geography and weather. But knowing Delhi has historically been a recipient of dust-laden winds and storms blowing across loose soil on 91,000 sq. Km of alluvial plains mostly now in Pakistan, it would seem that a deep wooded forest belt around Delhi could help drastically and permanently reduce chronic air pollution in the city due to dust. Even as the forest is growing over 10-15 years, tens of thousands of people will be dying unless immediate measures are implemented to cut down emissions from all sources as well. Detailed Emission Control Strategies for sustainable and massive reduction of air pollution in Delhi need to be evolved as described.

Table 12. Recommended Emission Control Strategies

Acknowledgement

The sustained interest and help of Dr E.A.S Sarma and Dr. R.Krishnan is gratefully acknowledged.

References

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Appendix “A”

Typical Air Pollution Data Sheet for This Longitudinal Study

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