Air quality studies over national capital territory Delhi using polyphemus modeling system

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AIR QUALITY STUDIES OVER NATIONAL CAPITAL TERRITORY DELHI USING POLYPHEMUS

MODELING SYSTEM

SAURABH KUMAR

CENTRE FOR ATMOSPHERIC SCIENCES INDIAN INSTITUTE OF TECHNOLOGY DELHI

OCTOBER 2019

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© Indian Institute of Technology Delhi (IITD), New Delhi, 2019

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AIR QUALITY STUDIES OVER NATIONAL CAPITAL TERRITORY DELHI USING POLYPHEMUS

MODELING SYSTEM

by

Saurabh Kumar

Centre for Atmospheric Sciences

Submitted

in fulfillment of the requirements of the degree of Doctor of Philosophy to the

Indian Institute of Technology Delhi

October 2019

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Dedicated to My Grandparents

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Certificate

This is to certify that the thesis entitled “Air Quality Studies over National Capital Territory Delhi using Polyphemus Modeling System” being submitted by Mr. Saurabh Kumar to the Indian Institute of Technology Delhi for the award of the degree of DOCTOR OF PHILOSOPHY is a record of the original bonafide research carried out by him. He has worked under our sustained guidance and supervision and has fulfilled the requirements for the submission of the thesis. The results presented in this thesis have not been submitted in part or full to any other University or Institute for award of any degree or diploma.

Prof. Maithili Sharan Prof. (Mrs.) Pramila Goyal

New Delhi October 2019

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Acknowledgements

The successful completion of any work that we take up in life is never an individual effort. This thesis is no different. I would like to express my deepest gratitude to my thesis supervisors, Prof.

Maithili Sharan and Prof. (Mrs.) Pramila Goyal, Centre for Atmospheric Sciences (CAS), Indian Institute of Technology Delhi, for their invaluable guidance, suggestions and endless encouragement. They have always given me the freedom to pursue my interests and provided me with insightful suggestions and support in developing independent thinking and research skills.

They have been an exceptional mentor, and I appreciate both our professional and personal conversations over the years. The knowledge and wisdom, I have gained from two stalwarts will forever guide me in education and life.

I would like to extend my sincere gratitude to my SRC members: Prof. A. D. Rao, Prof. R. K.

Sharma (Department of Mathematics) for generously sharing their knowledge and time. I would also like to thank Prof. (Mrs.) Manju Mohan, Head, CAS, IIT Delhi for providing all the essential facilities in the Centre. I would also like to express my gratitude to the CRC Chairperson, Prof.

Krishna Achuta Rao for his valuable support and suggestions. I am also grateful to all the faculty members of the Centre including Prof. U. C. Mohanty, Prof. O.P. Sharma, Dr. H. C. Upadhyay, Dr. S. Dey, Dr. S. B. Roy, Dr. Vimlesh Pant, Dr. S. K. Mishra, Dr. Dilip Ganguly, and Dr. S.

Sahany for their help and suggestions.

I gratefully acknowledge the financial support received from CSIR-UGC fellowship, J. C.

Bose fellowship to Prof. Maithili Sharan from DST-SERB.

I would like to thank and acknowledge the National Centre for Atmospheric Research (NCAR), the USA and National Centers for Environmental Prediction (NCEP), the USA for providing the model and reanalysis data sets. I would also like to thank and acknowledge ENPC- INRIA-EDF R&D, France for providing the model (http://cerea.enpc.fr/polyphemus/). Central Pollution Control Board (CPCB) and Delhi Pollution Control Board (DPCC) are also acknowledged for providing the air pollutants concentration data. India Meteorological Department (IMD) is highly appreciated and acknowledged for providing the meteorological data sets.

I would like to thank my friends and colleagues Dr. Anikender Kumar, Dr. Dhirendra Mishra, Dr. Kanhu Pattanayak, Dr. Pushpraj Tiwari, Dr. Sushant Das, Dr. Vijay Kumar, Dr.

Abhisekh Lodh, Dr. Himanshu Pradhan, Dr. Rati Sindhwani, Dr. Ragi, Dr. Raj Rani, Dr. Ram Singh, Dileep, Jismy, Ankur, Abhishek Upadhyay, Roshni, Ravi Prakash, Pawan, Puneet, Amit, Shivansh and Indranil for their active cooperation. Their nice company made my stay at IIT pleasant and memorable. I also thank the whole staff of Centre for Atmospheric Sciences especially Mr. L. S. Negi, Mr. V. K. Kaushik, Mr. Narender, Mrs. Neelam, Namita, Sachin, Mr.

Dataram and Vikas for their help and support.

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I am indebted to my best friends Amit, Dr. Gavendra Pandey, Dr. Piyush Srivastava, Sourabh and Sathiyaseelan with whom I shared my joy and sorrows during the long period of the research work. They provided me constructive criticism and inspiring discussions, which helped me to develop a broader perspective to my thesis.

I convey special thanks to all my friends, especially Dr. Rahul, Dr. Yadvendra, Dr.

Shasikant, Amit Jaiswal, Rakesh, Samit Choudhary, Sourav Anand, Shiv Shankar, Capt. Jayakant Shukla, Nutan Shukla for their whole hearted support and encouragement during this endless period of PhD. I will always miss the pleasant days spent with my dear friends.

Words cannot completely express my love and gratitude to my family members who have supported and encouraged me through this journey. I owe a lot to my parents, uncles and aunts who encouraged and helped me at every stage of my personal and academic life, and longed to see this achievement come true. I would like to thank my parents, sisters Dr. Rashmi, Anjali, Bhanu, Sayona, Nancy, Rose and Priyanka, brothers Dr. Rajeev, Rahul, Aditya, Prashun, Harsh, Krish and Sukrit; sisters-in-law Dr. Goldi and Rashmi Priyanka for their life-long support, everlasting love, and sacrifices, which sustained my interest in research and motivated me towards the successful completion of this study.

Finally, I thank the almighty God for the passion, strength, perseverance and the resources to complete this study.

New Delhi Saurabh Kumar

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ABSTRACT

Tremendous economic growth and rising population in urban cities of India are causing a significant deterioration in the air quality. In the past few years, particularly, the impact of local and urban air pollution and its effect on global climate change has been a point of great concern. Thus, in order to further plan the mitigation policies; firstly it is important to investigate the sources of air pollution, their corresponding emission contribution in the form of emission inventories and their impact on air quality. The study would thereafter be helpful in reducing the air pollution levels in urban cities while striving the economic growth. In view of the impact of tremendous growth on the ambient environment, the study of National Capital Region (NCR), which encapsulates Delhi and its satellite cities Gurgaon, Noida, Faridabad, Sonipat, Bahadurgarh, has been undertaken in this thesis. Delhi, the capital city of India, is one of the fastest growing economic centres of South East Asia, ranks among the most polluted cities of the world. In addition to this, the neighbouring satellite cities like Noida, Faridabad, Gurgaon due to their proximity with Delhi, better connectivity and their immaculate infrastructure has made the commuting between cities an easy ride. Thus, in this study, air quality modelling of NCR has been undertaken.

Mathematical models are being used extensively to predict/estimate pollutant concentrations, which in turn help in air quality studies, impact assessment and mitigation processes. The dispersion of the pollutants in the atmosphere driven by the meteorological parameters and the undergoing chemical processes. Accordingly, the concentration of the pollutants influenced by the interaction of meteorology and chemical changes. The literature review with respect to the above topics along with the air quality models used in the present study is discussed. Thus, the first chapter forms the background and motivation behind the

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problem undertaken in the thesis.

The pollutants emitted in the atmosphere are transported, dispersed or deposited by meteorological and topographical conditions. Thus, meteorology plays a very significant role in air quality modelling studies. Therefore, firstly it is important to comprehensively quantify the meteorological conditions over Delhi and its surrounding areas, which falls in the category of sub-tropical climatic conditions with very hot summers (the maximum temperature ranges from 41–45 °C) and cold and dry winters. Thus, Chapter 2 examines the sensitivity of the performance of the Weather Research and Forecast (WRF) model for various Land Surface model (LSM) and Planetary Boundary Layer (PBL) parameterization schemes over the study domain NCR for 6-days summer and 6 days in the winter season of 2010. The model estimated surface temperatures, wind speeds, potential temperature profiles and wind speed profiles are compared with the observations from India Meteorological Department and Wyoming Weather Web data archive at VIDP and VIDD. Comparison between simulated and observed data was scrutinized through statistical measures.

Apart from meteorological fields, the identification of pollutant-emitting sources is equally important to quantify the air quality of a region. Therefore in the subsequent chapters 3 and chapter 4, a gridded bottom-up regional emission inventory of criteria pollutants namely carbon monoxide (CO), sulphur dioxide (SO2), nitrogen oxides (NOx) and particulate matter (PM10) for Delhi and its surrounding areas (Gurgaon, Faridabad, Bahadurgarh, Ghaziabad, Noida, Greater Noida, Sonipat) together known as National Capital Region (NCR) has been used in the Chemical Transport Model (CTM) Polyphemus/Polair3D. The regional emission inventory in the study area has been divided into grids of size 2km x 2km between the 76.76°E to 77.46°E longitude and 28.30°N to 29.0°N latitude centred at metropolitan area, Delhi. The bottom-up gridded emission inventory includes major emission

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sources of the region, namely vehicular exhaust, road-dust re-suspension, domestic, industrial, power plants, brick kilns, aircraft and waste sectors. Moreover, emissions from global emissions data come from the Reanalysis of the Tropospheric (RETRO), and Emission Database for Global Atmospheric Research (EDGAR) has also been utilized to simulate the different criteria pollutants concentration. Further, the simulated concentration of criteria pollutants using EDGAR and regional emission inventory is evaluated with the available observed concentration in the study domain.

In chapter 5, WRF/Chem is the Weather Research and Forecasting model coupled with Chemistry has been used to simulate concentration over the study domain NCR for 6- days in summer and winter period of 2010. Two sets of simulations have been performed to assess the air pollutant concentration of O3, CO, NOX and particulate matters (PM2.5, PM10).

In the first set of simulation global EDGAR emission inventory has been utilized. In the second set of simulation, the prepared regional emission inventory has been implemented in the WRF-Chem model over Delhi, NCR Domain. Finally, the comparison of pollutant concentrations with Edgar and regional NCR inventories has been validated with the observed at three monitoring stations in Delhi, NCR. Inter-comparison of WRF/Chem and Polyphemus/Polair3D performance towards the simulation of different criteria pollutant is also carried out.

Finally, chapter 6 yields the conclusions drawn from the study undertaken in the thesis and suggestions for future work.

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सार

भारत के शहरी े ों म अ िधक आिथक िवकास और बढ़ती आबादी हवा की गुणव ा म मह पूण िगरावट का कारण बन रही है। िपछले कुछ वष म, िवशेष प से, थानीय और शहरी वायु दूषण का भाव और वैि क जलवायु प रवतन पर इसका भाव ब त िचंता का

िवषय रहा है। इस कार, शमन नीितयों को आगे की योजना बनाने के िलए; सबसे पहले वायु

दूषण के ोतों की जांच करना मह पूण है, उ जन आिव ार के प म उनके संबंिधत उ जन योगदान और वायु गुणव ा पर उनके भाव। इसके बाद का अ यन आिथक िवकास को गित दान करते ए शहरी े ों म वायु दूषण के र को कम करने म सहायक होगा।

प रवेशी वातावरण पर जबरद वृ के भाव के म ेनजर, रा ीय राजधानी े (NCR) का

अ यन, जो िद ी और उसके नजदीकी शहरों गुड़गांव, नोएडा, फरीदाबाद, सोनीपत, बहादुरगढ़ का अ यन करता है, इस थीिसस म िकया गया है। भारत की राजधानी िद ी, दि ण पूव एिशया के सबसे तेजी से बढ़ते आिथक क ों म से एक है, जो दुिनया के सबसे दूिषत शहरों

म शुमार है। इसके अलावा, िद ी के साथ िनकटता, बेहतर कने िवटी और उनके बेदाग बुिनयादी ढांचे के कारण नोएडा, फरीदाबाद, गुड़गांव जैसे पड़ोसी शहरों ने े ों के बीच आवागमन को आसान बना िदया है। इस कार, इस अ यन म, एनसीआर की वायु गुणव ा मॉडिलंग की गई है।

दूषक सां ता का अनुमान लगाने के िलए गिणतीय मॉडल का बड़े पैमाने पर उपयोग

िकया जा रहा है, जो बदले म वायु गुणव ा अ यन, भाव मू ांकन और शमन ि याओं म मदद करते ह। वायुमंडल म दूषकों का फैलाव मौसम संबंधी मापदंडों और चल रही रासायिनक

ि याओं से होता है। तदनुसार, मौसम िव ान और रासायिनक प रवतनों की बातचीत से

भािवत दूषकों की एका ता। वतमान अ यन म उपयोग िकए गए वायु गुणव ा मॉडल के

साथ उपरो िवषयों के संबंध म सािह समी ा की चचा की गई है। इस कार, पहला अ ाय थीिसस म की गई सम ा के पीछे की पृ भूिम और ेरणा बनाता है।

वायुमंडल म उ िजत दूषकों को मौसम िव ान और थलाकृितक थितयों ारा ले

जाया, फैलाया या जमा िकया जाता है। इस कार, मौसम िव ान वायु गुणव ा मॉडिलंग अ यनों

म ब त मह पूण भूिमका िनभाता है। इसिलए, सबसे पहले िद ी और इसके आसपास के े ों

पर मौसम संबंधी थितयों को ापक प से िनधा रत करना मह पूण है, जो ब त गम

ी काल (41-45 िड ी से यस से अिधकतम तापमान) और ठंड और शु के साथ उपो किटबंधीय जलवायु प र थितयों की ेणी म आता है। सिदयों। इस कार, अ ाय 2

िविभ भूिम भूतल मॉडल (LSM) के िलए मौसम अनुसंधान और पूवानुमान (WRF) मॉडल के

दशन की संवेदनशीलता की जांच करता है और 6 िदन की गिमयों और 6 िदन सिदयों के

मौसम के िलए ैनेटरी बाउंडी लेयर (PBL) मानकीकरण योजनाओं 2010 का अ यन डोमेन एनसीआर म िकया गया है। मॉडल का अनुमान सतह के तापमान, हवा की गित, संभािवत तापमान ोफाइल और हवा की गित ोफाइल की तुलना भारत के मौसम िवभाग और VIDP

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और VIDD म ोिमंग वेदर वेब डेटा सं ह से िकया गया है। सां कीय उपायों के मा म से

कृि म और मापे गए डेटा के बीच तुलना की गई।

मौसम संबंधी े ों के अलावा, दूषक-उ जक ोतों की पहचान एक े की वायु

गुणव ा को िनधा रत करने के िलए समान प से मह पूण है। इसिलए बाद के अ ाय 3 और अ ाय 4 म, िद ी और उसके आसपास के िलए काबन मोनोऑ ाइड (CO), स र डाइऑ ाइड (SO₂), नाइटोजन ऑ ाइड (NOx) और पािटकुलेट मैटर (PM10) जैसे मापदंड दूषकों के एक िनचले तल वाली े ीय उ जन सूची। े ों (गुड़गांव, फरीदाबाद, बहादुरगढ़, गािजयाबाद, नोएडा, ेटर नोएडा, सोनीपत) को एक साथ रा ीय राजधानी े (NCR) के प म जाना जाता है, का उपयोग रासायिनक प रवहन मॉडल (CTM) पॉलीफेमस / Polair3D म

िकया गया है। अ यन े म े ीय उ जन सूची को 76.76 ° E से 77.46 ° E देशांतर और 28.30 ° N से 29.0 ° N अ ांश के बीच महानगरीय े , िद ी म 2km x 2km के आकार म

िवभािजत िकया गया है। बॉटम-अप ि िडड एिमशन इ टी म े के मुख उ जन ोत शािमल ह, जैसे वाहन िनकास, सड़क-धूल पुन: िनलंबन, घरेलू, औ ोिगक, िबजली संयं , ईंट भ े, िवमान और अपिश े । इसके अलावा, वैि क उ जन डेटा से उ जन टोपो े रक (RETRO) के Reanalysis से आते ह, और उ जन के िलए वैि क वायुमंडलीय अनुसंधान (EDGAR) डेटाबेस भी िविभ मानदंडों दूषकों एका ता का अनुकरण करने के िलए उपयोग

िकया गया है। इसके अलावा, EDGAR और े ीय उ जन सूची का उपयोग कर मापदंड दूषकों की िस ुलेटेड एका ता का मू ांकन अ यन डोमेन म उपल अवलोकन एका ता

के साथ िकया जाता है।

अ ाय 5 म, WRF-Chem वेदर रसच और रसायन िव ान के साथ यु त पूवानुमान मॉडल का उपयोग अ यन डोमेन एनसीआर म कंसंटेशन को अनुकरण करने के िलए 2010 के 6 िदन गिमयों और सिदयों की अविध म िकया गया है। O3, CO, NOx और पािटकुलेट मैटर (PM 2.5, PM 10) की वायु दूषक कंसंटेशन का िसमुलेशन दो सेटों का आकलन करने के

िलए दशन िकया गया है। िसमुलेशन के पहले सेट म वैि क EDGAR उ जन सूची का उपयोग

िकया गया है। िसमुलेशन के दूसरे सेट म, तैयार े ीय उ जन सूची को िद ी, एनसीआर डोमेन पर WRF-Chem मॉडल म लागू िकया गया है। अंत म, िद ी और रा ीय राजधानी े के तीन िनगरानी ेशनों म देखे गए EDGAR और े ीय एनसीआर सूची के साथ दूषक कंसंटेशन की तुलना को मा िकया गया है। WRF-Chem और Polyphemus/Polair3D के

दशन की अंतर-तुलना िविभ मानदंडों के अनुकरण के िलए दिशत की गयी है।

अंत म, अ ाय 6 म भिव के काम के िलए थीिसस और सुझावों म िकए गए अ यन से िनकाले गए िन ष िमलते ह।

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List of Figures

Figure 1.1 A schematic diagram of an artificial neural network. 19 Figure 1.2 Classification of atmospheric dispersion models. 20 Figure 1.3 Flowchart for air quality modeling system. 22

Figure 1.4 Schematic diagram of different working stages of Polyphemus/Polair3D air quality model (adopted from Mallet et.

al, 2007).

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Figure 2.1 Schematic diagram for Advanced Research WRF (ARW) solver of the WRF modelling system (adopted from Wang et al., 2008).

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Figure 2.2 WRF nested modelling domain (Innermost domain containing NCR Delhi region).

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Figure 2.3 Indian Daily Weather Report (IDWR) at 8:30 IST (0300 UTC) for 19^th-24^th June 2010.

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Figure 2.4 Indian Daily Weather Report (IDWR) at 8:30 IST (0300 UTC) for 19^th-24^th December 2010.

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Figure 2.5 Diurnal variation of surface temperature at VIDD station (Safdarjung).

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Figure 2.6 Diurnal variation of surface temperature at VIDP station (IGI Airport).

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Figure 2.7 Diurnal variation of relative humidity during summer (Left Panels) and winter (Right Panels) at IGI airport (VIDP).

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Figure 2.8 Diurnal variation of relative humidity during summer (Left Panels) and winter (Right Panels) at Safdarjung (VIDD).

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Figure 2.9 Diurnal variation of wind speed at VIDD station (Safdarjung). 67

Figure 2.10 Diurnal variation of surface wind speed at VIDP station (IGI 70

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vi Airport).

Figure 2.11 Wind Roses (Blowing to) during 19^th -24^th Dec 2010 at IGI airport with NOAH-LSM.

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Figure 2.12 Wind Roses (Blowing to) during 19^th -24^th June 2010 at VIDP (IGI) using NOAH-LSM.

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Figure 2.13 Wind Roses (Blowing to) during 19^th -24^th Dec 2010 at VIDP (IGI) using RUC-LSM.

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Figure 2.14 Wind Roses (Blowing to) during 19^th -24^th June 2010 at VIDP (IGI) using RUC-LSM.

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Figure 2.15 Difference of flow fields (Circulation and temperature) w.r.t global analysis during winter case at 00 UTC, June 19^th, 2010 using RUC LSM and (a) YSU (b) ACM2 (c) MYJ (d) MRF (e) BouLac (f) MYNN3 PBL schemes.

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Figure 2.16 Difference of flow fields (Circulation and temperature) w.r.t global analysis during winter case at 00 UTC, JUNE 19^th, 2010 using NOAH LSM and (a) YSU (b) ACM2 (c) MYJ (d) MRF (e) BouLac (f) MYNN3 PBL schemes.

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Figure 2.17 Difference of flow fields (Circulation and temperature) w.r.t global analysis during winter case at 12 UTC, June 19^th, 2010 using RUC LSM and (a) YSU (b) ACM2 (c) MYJ (d) MRF (e) BouLac (f) MYNN3 PBL schemes.

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Figure 2.18 Difference of flow fields (Circulation and temperature) w.r.t global analysis during winter case at 12 UTC, June 19^th, 2010 using NOAH LSM and (a) YSU (b) ACM2 (c) MYJ (d) MRF (e) BouLac (f) MYNN3 PBL schemes.

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Figure 2.19 Difference of flow fields (Circulation and temperature) w.r.t global analysis during winter case at 00 UTC, December 19^th, 2010 using RUC LSM and (a) YSU (b) ACM2 (c) MYJ (d) MRF (e) BouLac (f) MYNN3 PBL schemes.

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Figure 2.20 Difference of flow fields (Circulation and temperature) w.r.t global analysis during winter case at 00 UTC, December 19^th, 2010 using NOAH LSM and (a) YSU (b) ACM2 (c) MYJ (d) MRF (e) BouLac (f) MYNN3 PBL schemes.

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Figure 2.21 Difference of flow fields (Circulation and temperature) w.r.t global analysis during winter case at 12 UTC, December 19^th,

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2010 using RUC LSM and (a) YSU (b) ACM2 (c) MYJ (d) MRF (e) BouLac (f) MYNN3 PBL schemes.

Figure 2.22 Difference of flow fields (Circulation and temperature) w.r.t global analysis during winter case at 00 UTC, December 19^th, 2010 using NOAH LSM and (a) YSU (b) ACM2 (c) MYJ (d) MRF (e) BouLac (f) MYNN3 PBL schemes.

84

Figure 2.23 Vertical profiles of Potential temperature at VIDD (Safdarjung)

at 00 UTC and 12 UTC, 19^th December 2010. 86 Figure 2.24 Vertical profiles of Potential temperature at VIDD (Safdarjung)

at 00 UTC and 12 UTC, 19^th June 2010.

87

Figure 2.25 Vertical profiles of Wind Speed at VIDD (Safdarjung) at 00 UTC, 19^th June 2010.

88

Figure 2.26 Vertical profiles of Wind Speed at VIDD (Safdarjung) at 00 UTC and 12 UTC, 19th December 2010.

89

Figure 2.27 Vertical profiles of Wind Direction at VIDD (Safdarjung) at 00 UTC 19th June 2010.

90

Figure 2.28 Vertical profiles of Wind Direction at VIDD (Safdarjung) at 00 UTC and 12 UTC, 19th December 2010.

91

Figure 3.1 Inner domain of regional emission inventory for the study area.

( ) represents the air quality monitoring stations.

98

Figure 3.2 Schematic diagram for the pre-processing stage of the Polyphemus system.

107

Figure 3.3 Spatial variation of mean anthropogenic surface emissions (ug/m2/s) of CO, NO2, SO2 and NO over the study domain during the simulation period using regional emission inventory (Left panels) and EDGAR-HTAP_V2 (Right panels) respectively.

116

Figure 3.4 The mean simulated concentration of gaseous species O3, NO2, CO and SO2 using regional emission inventory (a, c, e, g) and EDGAR-HTAP_V2 (b, d, f, h) during summer simulation period 2010.

118

Figure 3.5 The mean simulated concentration of gaseous species O3 (a) NO2 119

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(c), CO (e) and SO2 (g) using regional emission inventory and EDGAR-HTAP_V2 O3 (b), NO2 (d), CO (f), SO2 (h)) during winter simulation period 2010.

Figure 3.6 Comparison of Diurnal Variation of simulated O3 using regional and EDGAR-HTAP_V2 emission inventory with monitored O3 at IGI, ITO sites in the study area during the winter study period.

122

Figure 3.7 Comparison of Diurnal Variation of simulated NO2 using regional and EDGAR-HTAP_V2 emission inventory with monitored NO2 at IGI and ITO in the study area during the winter study period.

123

Figure 3.8 Comparison of temporal variation of simulated NO2 using (a) EDGAR-HTAP_V2 and (b) regional emission inventory with monitored NO2 at IGI during the winter study period of the year 2010.

125

Figure 3.9 Comparison of temporal variation of simulated NO2 using (a) EDGAR-HTAP_V2 and (b) regional emission inventory, with observed NO2 at ITO during the winter study period of the year 2010.

126

Figure 3.10 Comparison of observed and simulated 8 hourly average concentration of O3 with (regional emission inventory and EDGAR_HTAP_V2) at IGI and ITO during winter and summer simulation period of the year 2010.

128

Figure 3.11 Comparison of NO2, CO and O3 average absolute differences between base case and the 50% (ordinate) and 100% (abscissa) reduction scenarios, during June (left panels) and December (right panels) 2010; each dot represents a value at the surface grid point; ordinate scale is one half of abscissa scale, so dots located along the diagonal represent linear response to emission reduction.

131

Figure 3.12 Maximum relative potency (Imax) due to the abatement for emissions of major precursors (NOX, VOCs) of O3 during summer and winter simulation period over the study region.

134

Figure 4.1 Spatial Average concentration plot of PM2.5 (a) using EDGAR- HTAP, (b) Regional Emission inventory during June-2010

149

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Figure 4.2 Spatial Average concentration plot of PM2.5 (a) using EDGAR- HTAP, (b) Regional Emission inventory during December-2010 simulation period.

150

Figure 4.3 Spatial Average concentration plot of PM10 (a) using EDGAR- HTAP (b) Regional Emission inventory during June-2010 simulation period.

151

Figure 4.4 Spatial Average concentration plot of PM10 (a) using EDGAR- HTAP (b) Regional Emission inventory during December-2010 simulation period.

151

Figure 4.5 Bar-charts for the Concentrations of each species during (a) summer and (b) winter simulation period.

152

Figure 4.6 Mass distribution (in μg.m-3) during the summer simulation period using (a) EDGAR-HTAP_V2 and (b) regional emission inventory.

154

Figure 4.7 Mass distribution (in μg.m-3) during the winter simulation period using (a) EDGAR-HTAP_V2 and (b) regional emission inventory.

154

Figure 4.8 Number distribution (in μ.m) during the summer simulation period using (a) EDGAR-HTAP_V2 and (b) Regional emission inventory.

156

Figure 4.9 Number distribution (in μ.m) during the winter simulation period using (a) EDGAR-HTAP_V2 and (b) regional emission inventory.

156

Figure 4.10 Time series comparison of observed PM10 and simulated PM10 at Dwarka using regional and Edgar emission inventories during (a) summer and (b) winter conditions. (••••) Observed PM10; (----) simulated PM10 using regional EI and (……) simulated PM10

using EDGAR EI.

158

Figure 4.11 Time series comparison of observed PM10 and simulated PM10 159

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using regional and Edgar emission inventories. (••••) Observed PM10; (---) simulated PM10 using Regional EI and (……) simulated PM10 using EDGAR EI.

Figure 5.1 Time series comparison of simulated O3 from WRF-Chem and Polair3D with Observed O3 at ITO during (a) summer and (b) winter case.

172

Figure 5.2 Time series comparison of simulated NO2 from WRF-Chem and Polair3D with Observed NO2 at ITO during (a) summer and (b) winter case.

174

Figure 5.3 Time series comparison of simulated CO from WRF-Chem and Polair3D with Observed CO at ITO during (a) summer and (b) winter case.

175

Figure 5.4 Time series comparison of simulated (a) O3 and (b) NO2 from WRF-Chem and Polair3D with Observed O3 and NO2 at IGI Airport during winter case.

176

Figure 5.5 The average concentration plot of O3 (First row) and NO2

(Second row) over the study domain using (a) WRF-Chem and (b) Polyphemus during summer case

178

Figure 5.6 The average concentration plot of PM2.5 (First row) and PM10

(Second row) over the study domain using (a) WRF-Chem and (b Polyphemus during the summer simulation case.

180

Figure 5.7 The average concentration of O3 (First row) and NO2 (Second row) using (a) WRF-Chem and (b) Polyphemus during the winter simulation case.

181

Figure 5.8 The average concentration plot of PM2.5 (First row) and PM10

(Second row) over the study domain using (a) WRF-Chem and (b) Polyphemus/Polair3D during the winter case using EDGAR emission inventory.

183

Figure 5.9 Time series comparison of simulated O3 from WRF-Chem and Polair3D with Observed O3 at ITO during (a) summer and (b) winter case.

184

Figure 5.10 Time series comparison of simulated NO2 from WRF-Chem and Polair3D with that Observed at ITO during (a) summer and (b) winter.

186

Figure 5.11 Time series comparison of simulated (a) O3 and (b) NO2 from WRF-Chem and Polair3D with Observed NO2 at IGI during

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Figure 5.12 Time series comparison of simulated PM10 from WRF-Chem and Polair3D with Observed PM10 at Dwarka during (a) summer and (b) winter case.

190

Figure 5.13 Time series comparison of simulated PM10 from WRF-Chem and Polair3D with Observed PM10 at IGI Airport during winter case.

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List of Tables

Table 2.1 Different physics options considered in WRF. 40

Table 2.2 Statistical parameters correlation coefficient, Root Mean Square Error (RMSE) and Normalized Mean Square Error (NMSE) for surface temperature during December 19^th -24^th and June 19^th -24^th, 2010 simulation period at Safdarjung (VIDD) using NOAH LSM.

55

Table 2.3 Statistical parameters correlation coefficient, RMSE and NMSE for surface temperature during December 19^th -24^th and June 19^th -24^th, 2010 simulation period at Safdarjung (VIDD) using RUC LSM.

56

Table 2.4 Statistical parameters correlation coefficient, RMSE and NMSE for surface temperature during December 19^th -24^th and June 19^th -24^th, 2010 simulation period at IGI Airport (VIDP) using NOAH-LSM.

58

Table 2.5 Statistical parameters correlation coefficient, RMSE and NMSE for surface temperature during December 19^th -24^th and June 19^th -24^th, 2010 simulation period at IGI Airport (VIDP) using RUC LSM.

59

Table 2.6 Statistical measures between observed and simulated Relative Humidity during winter and summer cases at VIDP.

63

Table 2.7 Statistical parameters correlation coefficient, RMSE and NMSE for surface wind speed during December 19^th -24^th and June 19^th -24^th, 2010 simulation period at Safdarjung (VIDD) using NOAH LSM.

65

Table 2.8 Statistical parameters correlation coefficient, RMSE and NMSE for surface wind speed during December 19^th -24^th and June 19^th -24^th, 2010 simulation period at Safdarjung (VIDD) using RUC LSM.

66

Table 2.9 Statistical parameters correlation coefficient, RMSE and NMSE for surface wind speed during December 19^th -24^th and June 19^th -24^th, 2010 simulation period at IGI Airport (VIDP) using NOAH LSM.

68

Table 2.10 Statistical parameters correlation coefficient, RMSE and NMSE for surface wind speed during December 19^th -24^th and June 19^th -24^th, 2010 simulation period at IGI Airport (VIDP) using RUC LSM.

69

Table 3.1 Various parameterization schemes options opted in the WRF study. 109

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Table 3.2 Model configurations for the study domain.

111

Table 3.3 Modified distribution of gases applied for the regional and EDGAR- HTAP_V2 emission inventory according to different height levels based on the source category (De Meij et al., 2006; Pregger and Friedrich, 2009).

112

Table 3.4 Statistical measures to assess the model performance using regional and global emission inventory with respect to monitored and simulated 8hr average ozone.

127

Table 4.1 Modified distribution of aerosol applied for the regional and EDGAR-HTAP_V2 emission inventory according to different height levels based on the source category (De Meij et al., 2006;

Pregger and Friedrich, 2009).

144

Table 4.2 Model configuration for the study domain. 147 Table 5.1 Various parameterization schemes used in the study. 167

Table 5.2 Statistical measures between simulated and observed O3 at ITO during summer and winter case from EDGAR and Regional Emission inventory (REI).

169

Table 5.3 Statistical measures between simulated and observed NO2 at ITO during summer and winter case from EDGAR and Regional Emission inventory (REI).

169

Table 5.4 Statistical measures between simulated and observed O3; NO2 at IGI airport in winter case from EDGAR and REI.

170

Table 5.5 Statistical measures between simulated and observe O3 at ITO during summer and winter case.

173

Table 5.6 Statistical measures between simulated and observed O3; NO2 at IGI during winter case.

177

Table 5.7 Statistical measures between simulated and observe O3 at ITO during summer and winter case.

185

Table 5.8 Statistical measures between simulated and observed NO2 at ITO

during summer and winter case. 187

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Table 5.9 Statistical measures between simulated and observed O3; NO2 at IGI during winter case.

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LIST OF ABBREVIATIONS

ABL Atmospheric Boundary Layer

AIIMS All India Institute of Medical Sciences

AQM Air Quality Model

ARW Advanced Research WRF

AQI Air Quality Index

CCN Cloud Condensation Nuclei

CMAQ Community Model for Air Quality

CO Carbon Monoxide

CO2 Carbon dioxide

CPCB Central Pollution Control Board

CTM Chemical Transport Model

DPCC Delhi Pollution Control Committee

DSH Delhi Statistical Handbook

EDGAR Emission Database for Global Atmospheric Research

ESRL Earth System Research Laboratory

FAC2 Factor of 2

GrADS Grid Analysis and Display System

HCV Heavy Commercial Vehicle

IPCC Intergovernmental Panel on Climate Change

LCV Light Commercial Vehicle

LSM Land Surface Model

MOEF Ministry of Environment and Forest NAAQSs National Ambient Air Quality Standards

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NCAR National Center for Atmospheric Research NCEP National Center for Environmental Prediction

NCL NCAR Graphics Command Language

NCR National Capital Region

NMM Non-hydrostatic Mesoscale Model

NMSE Normalized Mean Square Error

NOAA National Oceanic and Atmospheric Administration

NOx Nitrogen oxides

PBL Planetary Boundary Layer

PM Particulate Matter

QNSE Quasinormal Scale Elimination

R Correlation Coefficient

REI Regional Emission Inventory

RETRO REanalysis of the TROpospheric

RIP Read/Interpolate/Plot

RMSE Root Mean Squared Error

SIREAM Size Resolved Aerosol Module

SL Surface Layer

SO2 Sulphur dioxide

VIDD Delhi (Safdarjang) Airport

VIDP Indira Gandhi International Airport (at Palam)

WHO World Health Organization

WRF Weather Research and Forecasting Model

Air quality studies over national capital territory Delhi using polyphemus modeling system

AIR QUALITY STUDIES OVER NATIONAL CAPITAL TERRITORY DELHI USING POLYPHEMUS

MODELING SYSTEM

SAURABH KUMAR

CENTRE FOR ATMOSPHERIC SCIENCES INDIAN INSTITUTE OF TECHNOLOGY DELHI

OCTOBER 2019

© Indian Institute of Technology Delhi (IITD), New Delhi, 2019

AIR QUALITY STUDIES OVER NATIONAL CAPITAL TERRITORY DELHI USING POLYPHEMUS

MODELING SYSTEM

by

Saurabh Kumar

Centre for Atmospheric Sciences

Submitted

in fulfillment of the requirements of the degree of Doctor of Philosophy to the

Indian Institute of Technology Delhi

October 2019

Dedicated to My Grandparents

Certificate

Prof. Maithili Sharan Prof. (Mrs.) Pramila Goyal

Acknowledgements

ABSTRACT

सार

Contents

List of Figures

List of Tables

LIST OF ABBREVIATIONS