UrbanEmissions.Info stands for (a) sharing knowledge on air pollution (b) science based air quality analysis (c) advocacy and awareness raising on air quality management and (d) building partnerships among local, national, and international air-heads.
What is Polluting Delhi’s Air?
Air pollution in (urban and rural) India is a growing public concern, and city of Delhi (its capital) is one of the most studied city with a disproportionate share of media attention. Yet, we do not seem to have decisive answers to simple questions like how polluted is the city, what are the main sources, and where to start to control pollution in the city. A review of Delhi’s air quality from 1990 to 2022 from data, sectoral, judicial, and institutional perspectives was published as a journal article in 2023. [main link]
NCAP was introduced in 2019 to address air pollution in India’s non-attainment cities. The current list of 131 cities are required to document (1) emission and pollution load via monitoring and modelling (2) activities necessary for all the known sources to achieve the pollution target (3) plans to build institutional capacity to manage the information flow and (4) ways to oversee the progress of various components. In support of the program, here are the data resources and synthesis reports.
- List of the non-attainment cities and designated airsheds (here)
- A review of the approved action plans (here)
- Download ambient monitoring needs information by airshed (here)
- Download GIS road lines and road density information by airshed (here)
- Download GIS urban built-up area files by airshed (here)
- Explore more here
Launched in August 2017, the APnA city program is designed to provide a starting point for understanding air pollution in urban agglomerations to support public dialogue and policy discussions. Architecture behind the APnA city program is carved and updated from our operational all India air quality forecasting platform.
We currently have stories for 60 Indian cities – Agra, Amritsar, Bengaluru, Bhopal, Bhubaneswar, Chandigarh-Ambala-Patiala, Chennai, Coimbatore, Dehra Dun, Indore, Jaipur, Kanpur, Kochi, Ludhiana, Nagpur, Patna, Pune, Raipur-Durg-Bhilai, Ranchi, Varanasi, Agartala, Ahmedabad, Allahabad, Asansol-Durgapur, Aurangabad, Dharwad-Hubli, Dhanbad-Bokaro, Gaya, Guwahati-Dispur, Gwalior, Hyderabad, Jamshedpur, Jodhpur, Kolkata-Howrah, Kota, Lucknow, Madurai, Mumbai, Nashik, Panjim-Vasco-Margao, Puducherry, Rajkot, Shimla, Srinagar, Surat, Thiruvananthapuram, Tiruchirapalli, Vadodara, Vijayawada-Guntur, and Visakhapatnam.
April, 2023: Monitoring is an exercise to measure ambient levels of air pollution in an area. The results indicate the status of quality of air we breathe and over a long term allows us to tease out spatial and temporal patterns that help support air pollution control policy. A reference note was published in 2018, as an attempt to explain some frequently asked questions on air pollution monitoring – What purpose does it serve? Is ambient monitoring the same as emissions monitoring? How does one monitor? How can we integrate “low-cost” monitors and satellite retrievals into policy dialogue? All the references used in this piece are from India, but the notes is relevant for other countries as well. Now, this information is available as a primer in multiple languages – English, Hindi (Hinglish), Telugu, Marathi, Bengali, Punjabi, Spanish, French, Russian, and Chinese [main link].
Summary of Air Quality in India between 1998-2020
Reanalyzed PM2.5 concentrations and modelled source contributions were analyzed to present (a) India’s % area exposed to various pollution bins in 1998-2020 (b) India’s % population exposed to various pollution bins in 1998-2020 (c) All India pollution maps for 1998-2020 (d) State and Union Territory maps for 1998-2020 (e) State pollution rank among the 36 States and Union Territories for 1998-2020 and (f) Average % source contributions at State level for 2017. All the maps, extracted data files, infographs, and animations are available here.
Open access journal article “Evolution of India’s PM2.5 Pollution Between 1998 and 2020 Using Global Reanalysis Fields Coupled with Satellite Observations and Fuel Consumption Patterns” and supplementary information is published here.
Air Quality Forecasts for India – State-level Summaries
Our forecasting system also includes a high-resolution medium-range meteorological system for the next 72-hours, processed through 3D-WRF meteorological model and the GFS meteorological fields, followed by chemical transport modeling using the CAMx modeling system and a detailed emissions inventory for anthropogenic and natural sources. The modeling domain covers the Indian subcontinent at a temporal resolution of 1 hour. Air quality information is available as tables, time series, and maps at national and state level. A glimpse of state-level summary of PM2.5 concentrations, source contributions, and meteorological data is below for the state of Uttar Pradesh. For other states and more, click here [main link].
Irrespective of the monitoring approaches and equipment, the initiation process requires an understanding of the pollution loads, mix of sources, and geography of the city to decide how much to monitor for better spatial representation and how many times to monitor for better
temporal representation. In this report, we defined (a) the size of NCAP city airsheds (b) the recommended number of ambient air quality monitoring sites in an airshed (c) the operational sampling frequency to support receptor-based source apportionment studies. These resources are necessary for strengthening the monitoring needs of an airshed to track pollution levels, to conduct receptor-model-based source apportionment studies, and to support long-term air quality management plans.
Here we present a repository of resource links on who is monitoring air quality in India, where is the data stored, how to access the data from the official and global repositories, and an illustrated note on how to interpret air quality data and what we can do. The crux of this repository is about India’s air quality. However, the links also lead to information for worldwide network of resources.
Same primer on air pollution monitoring in Hindi is available here.
I participated in a series of podcast recordings and online lectures during the COVID-induced/isolated-2020 and there were some frequently asked questions. While these questions sound simple, it was not easy to frame technical understanding into simple statements. This paper is dedicated to answer some of those questions on particulate matter (PM) and go over some basic material like size definitions, chemical composition, monitoring, source apportionment, health impacts, and modeling. Our working paper series covers case studies and general understanding of air pollution concepts.
This paper is curated to infuse a better understanding of air quality index (AQI), starting with what does it represent and how is it different from air quality to where to find real time data. While these questions sound simple, it was not easy to frame technical understanding into simple statements, so we took the liberty of adding some doodles. Our working paper series covers case studies and general understanding of air pollution concepts. You can download MS Excel based calculators using methodologies for seven (7) countries and instructional videos to use them, here.
Here we present a repository of resource links ranging from official portals; guidelines, acts, and rules documents; compiled statistics, maps, and other geospatial databases; satellite observations and tools; ambient air quality monitoring data from official and unofficial networks; global and regional emission inventories; global reanalysis fields; and global meteorological data fields and visualization portals, necessary for putting together energy, emissions, and air pollution analysis in India.
All the database links are also available as a PDF under the working paper series.
Abstract: We examine the health implications of electricity generation from the 2018 stock of coal-fired power plants in India, as well as the health impacts of the expansion in coal-fired generation capacity expected to occur by 2030. We estimate emissions of SO2, NOX, and particulate matter 2.5 μm (PM2.5) for each plant and use a chemical transport model to estimate the impact of power plant emissions on ambient PM2.5. Concentration-response functions from the 2019 Global Burden of Disease (GBD) are used to project the impacts of changes in PM2.5 on mortality. Current plus planned plants will contribute, on average, 13% of ambient PM2.5 in India. This reflects large absolute contributions to PM2.5 in central India and parts of the Indo-Gangetic plain (up to 20 μg/m3). In the south of India, coal-fired power plants account for 20–25% of ambient PM2.5. We estimate 112,000 deaths are attributable annually to current plus planned coal-fired power plants. Not building planned plants would avoid at least 844,000 premature deaths over the life of these plants. Imposing a tax on electricity that reflects these local health benefits would incentivize the adoption of renewable energy.
This PNAS (2021) article and others on India’s coal fired thermal power plants are available here.
India observed four lockdown phases between March and May 2020: (a) March 24 to April 14 (21 days); (b) April 15 to May 3 (19 days); (c) May 4 to May 17 (14 days); and (d) May 18 to May 31 (14 days). As the lockdown periods were extended, air quality improved at various degrees, not just in the big cities like Delhi, Mumbai, Kolkata, and Chennai, but across the country. We explored the air-quality trends in cities with at least one ambient monitoring station each, for all pollutants responsible for defining India’s official air quality index. The data is available here.
the WIRE, May 2019: I recently re-read the book Tipping Point by Malcolm Gladwell. I enjoyed reading it in 2003-04 and enjoyed going through it again, the long explanations, stories in stories, and how simple concepts led to big changes in various fields. One recurring theme in the book is about the three agents of change: (a) connectors – network of likeminded people; (b) mavens – information specialists; and (c) salesmen – persuaders. This got me thinking about parallels in the world of air pollution. I see myself as an atmospheric scientist, but where is my place in the field of air quality management? Similarly, what is the role of likeminded people in this space, who are trying to push for a change – moving from dirty air to clean blue skies? Following the same three-agents principle, I see three interlinking groups working in the field of air pollution: (a) Feeders (b) Drummers and (c) Changers. An illustrated version of the article is here.
Atmospheric science defines the air pollution problem as (a) a dynamic situation where the air is moving at various speeds with no boundaries and (b) a complex mixture of chemical compounds constantly forming and transforming into other compounds. With no boundaries, it is unscientific to assume that one can trap air, clean it, and release into the same atmosphere simultaneously. Access the paper which describes why the idea of vacuuming outdoor air pollution is unrealistic and some commentary notes.
A prerequisite to an air quality management plan is some idea of (a) how much is the pollution (monitoring trends) (b) where is the pollution (spatial trends) (c) who is contributing to the pollution (source trends) (d) when is the pollution (temporal trends) (e) what can we do about the pollution (control trends). This involves a long line of discussions, surveys, modeling, planning, and finally implementation of the decisions for better air quality. In this primer, we explain these concepts with examples, provide an understanding of the players involved in the process, an overview of the data needs from monitoring to modeling, and how to start to think about managing the information for better air quality.
For building an effective air pollution control plan, it is important to know the contribution of sources. This is not an easy process, as it involves many steps related to field experiments; to laboratory analysis; to collating information from surveys, maps, and literature; to statistical and predictive modeling; and to linking the results to pollution control planning. In this primer, we explain these concepts with examples. If you are planning a study, here is some background notes on information and decisions required to conduct source apportionment using the top-down (receptor-based) method and an overview of pros and cons of the top-down and bottom-up (emissions-based) methods.
November, 2017: Two months ago, we released the following infograph [PDF], pointing out that media and public interest in Delhi’s air quality peaks around Diwali, stays up there for a couple of weeks and slowly dies down towards the end of winter. This pattern is consistent with the past years as well. The graph plots “relative interest” in the topic of air pollution as quantified by Google searches. We wrote an accompanying article that expands on this issue in the WIRE. This reference note is an attempt to consolidate what we understand as the extent of fireworks burnt during Diwali 2017 in Delhi, its share in overall air quality during the event, and current role of judiciary in tackling this source. [main link]
Status of Air Monitoring in India
The continuous air monitoring data from all the publicly accessible stations operated and maintained by the Central Pollution Control Board (CPCB) and the State Pollution Control Boards is presented here as an online resource. All the data from these stations is available from CPCB website.
This blog piece explains ways in which this data can be accessed and processed for further applications.
We also present an assessment of what India needs to spatially, temporally, and statistically represent the ambient pollution in the urban and the rural areas – based on thumb rule proposed by CPCB and the district level urban and rural population (as per 2011 census). We estimate a need for 4,000 continuous monitoring stations (2,800 in the urban areas and 1,200 in the rural areas).
This resource link presents the sources of air quality data from official and unofficial portals and some repositories of the data for immediate use.
Open fires associated with agricultural residue clearing (after the seasonal harvests and a typical process to prepare for the next crop) and forest fires (associated with hot and dry conditions and some times intentional) is an important source of particulate and trace gas emissions. Detection of these fires is a complex methodology, made easy with the availability of a series of open satellite feeds. We utilize the NASA Worldview platform to visualize and access this information. Image to the right presents all open fires detected over the Indian Subcontinent in the last 24 hours (updated with VIIRS feed every 3 hours). A multi-pollutant emissions inventory, estimated using the location information and land-use databases (agricultural, forest, urban, water, arid, etc.) is available from UCAR-FINN program. For more details and to access archives, click here.