Launched in December 2017, the APnA city program is designed to provide a starting point for understanding air pollution in Indian cities. We released reports on emissions and dispersion modeling results for 50 1-million plus cities in the census. Main focus is on non-Delhi cities, putting together stories for cities with limited information, and taking the databases forward for public dialogue and policy discussions. The modeling database behind the APnA city program is updated and carved from our operational all India air quality modeling system, which is in-use for long term policy assessments and short term air quality forecasting. [main link]
20 Cities from 2017 – Agra, Amritsar, Bengaluru, Bhopal, Bhubaneswar, Chandigarh, Chennai, Coimbatore, Dehra Dun, Indore, Jaipur, Kanpur, Kochi, Ludhiana, Nagpur, Patna, Pune, Raipur, Ranchi, Varanasi
30 cities from 2019 – 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, Visakhapatnam
The All India air quality forecasting system interjects contributions of pollution for national, regional and urban areas, and trans-boundary, to better support a long-term air quality management plan and a short-term health alert system. The modeling domain covers the Indian sub-continent with a spatial resolution of 0.25° x 0.25° (~25 km 25 km) and a temporal resolution of 1 hour. The modeled meteorology and concentration data fields are updated everyday at approximately 21:00 (IST). Example system products from the WRF–CAMx chemical transport modeling system coupled with dynamic emissions inventories, include pollution maps of hourly and daily averages, time series of hourly average concentrations and modeled source contributions to the hourly PM2.5 average concentrations for all the 640 districts. More details @ India’s Air Quality Forecasts. System operational since June, 2016. [main link]
To support Delhi’s air pollution management, we forecast ambient concentrations for all the criteria pollutants for the next 72-hours. The modeling domain covers Delhi and its satellite cities with a spatial resolution of 0.01 degrees (~1 km) and a temporal resolution of 1 hour. The modeled meteorology and concentration data fields are updated everyday at approximately 21:00 (IST). Example system products from the WRF–CAMx chemical transport modeling system coupled with dynamic emissions inventories, include pollution maps of hourly and daily averages, time series of hourly average concentrations by district, and modeled source contributions to the hourly PM2.5 average concentrations. More details @ Delhi’s Air Quality Forecasts. System operational since June, 2016. [main link]
The All India air quality forecasting system interjects contributions of pollution for national, regional and urban areas, utilizing the WRF–CAMx chemical transport modeling system coupled with dynamic emissions inventories, and trans-boundary. The modeling domain covers the Indian sub-continent with a spatial resolution of 0.25° x 0.25° (~25 km 25 km) and aggregated to 640 districts. The 24-hr average modeled PM2.5 concentrations are also summarized by state for a quick look. The color code corresponds to the India national AQI methodology. The modeled meteorology and concentration data fields are updated everyday at approximately 21:00 (IST). For example, the state of West Bengal is presented here and other states are detailed @ India’s Air Quality Forecasts. [main link]
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 coming (spatial trends) (c) who is contributing to the pollution (source trends) (d) when is the pollution (temporal trends) (e) what can be do about the pollution (control tends). This involves a long line of discussions, surveys, modeling, planning, and finally implementation of the decisions for better air quality. We put together an easy to read primer on overall air quality management of these steps, 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. [main link]
For building an effective air pollution control plan, it is important to know the contribution of sources. This is not a easy process, as it involves many steps – some related to field experiments; some related to laboratory analysis; some related to collating information from surveys, maps, and literature; some related to (statistical and predictive) modeling; and some related to linking the results to pollution control planning. We structured these concepts into this primer on pollution source apportionment, explaining the ways to consolidate information from surveys, sampling, and modeling to estimate contribution of sources to ambient pollution, an understanding of benefits and limitations of such methods, and ways to support an informed air quality management plan. [main link]
March, 2018: Monitoring is an exercise to measure ambient levels of air pollution in an area. The results of which indicate the status of quality of air we breathe. Monitoring data, over a long term, is especially useful as it allows us to tease out patterns that help support air pollution control policy. With some paper and pencil rendering, this reference note is our attempt to explain air pollution monitoring – What purpose does it serve? Is ambient monitoring the same as emissions monitoring? How does one monitor? How do “low-cost” monitors fit in? All the references used in this piece are from India, but the notes is relevant for other countries as well. [main link]
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]
March, 2016: 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. Following some opinion pieces published online, this reference note is an attempt to put some information into perspective for one perpetual question, what are the sources of air pollution in Delhi? This is the most commonly asked question and also the most confusing and unanswered. Before we jump into blame games and laying down numbers, we also explain some basic principles of air pollution. [main link]
For air quality forecasts for the next 72 hours, click here.
A combination of satellites provide a cache of measurements, which are interpreted using the global chemical transport models to better represent the vertical mix of pollution and finally arrive at the ground-based estimates with the help of past ground-based measurements. The global dataset is available at 0.1° and 0.01° resolution, in multiple formats to import and use on multiple presentation platforms. We utilized the finer resolution data to build India specific maps (modeling domain of India’s air quality forecasting program) aggregated to state and district levels.
Browse here by state and by district to see evolution of PM2.5 concentrations between 1998 and 2016.
Gridded annual maps of PM2.5 concentrations by year between 1998 to 2016 are available here.
Gridded annual maps of % change in PM2.5 concentrations since 2000 are available here.
September, 2017: The continuous air monitoring data is presented here as an online resource, with data feeds from the publicly accessible stations with reference grade monitoring equipment, mostly operated and maintained by the Central Pollution Control Board (CPCB) and the State Pollution Control Boards. All the data from these stations is available from CPCB website. A secondary source to download archived data is @ http://www.openaq.org (this is a portal with open access to monitoring data from stations across the globe; and also allows you to download data for select station or city or time period; compare data between stations, between cities, visualize the trends spatially and temporally, and much more).
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). Browse the requirements by state and by district here.
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.