Launched in December 2017, the APnA city program is designed to provide a starting point for understanding air pollution in Indian cities.
The program currently includes reports on emissions and dispersion modeling results for 50 one-million plus cities in the census. The 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.
Data for twenty cities was released in 2017, and on August 1, 2019, data for 30 another cities was released.
Here is a short Q&A with Dr. Sarath Guttikunda regarding the project:
We often hear that access to data is an issue in India. What data streams do you use for the APnA city program and has data access improved over time?
If the agenda is to wait for the perfect data to come along to tick all the boxes, then we will be waiting for a long time. APnA stands for “air pollution knowledge assessments” – in other words, we are in the process of collating any knowledge possible on air pollution in a city. Access to more sources of information can only make the analysis better.
To build an emissions inventory for a city, we use data in all forms – numbers, maps, images, reports, journal articles, and oral communications. This is then processed through a chemical transport model, along with meteorology, to establish a pollution map covering all spatial and temporal scales. While this statement sounds easy, the process of converting information in various forms into what one needs at the end to build an emissions inventory is an involved process. We have documented these resources on the APnA page and also in a journal article published in Urban Climate. Some of these resources are free and some are paid-services (such as traffic information from google maps).
Over time, the methods have improved significantly. Analysis for a batch of 20 cities was released in 2017 using what was possible to gather at that time. Now, we are releasing analysis for 30 new cities (in 2019), which is more robust spatially and temporally. A consistent improvement in the methods is visible in the limited comparisons between measurements and modeled data.
Can you share a few examples of how APnA data can be used?
The APnA city program is designed to provide the following streams of information:
1. Assessment of total emission loads in the city’s urban airshed, for all the key pollutants – PM, SO2, NOx, CO, and VOCs
2. Identification of emission hot spots, which is accomplished by combining a variety of layers of information from meteorology, roads, population, and commercial activities
3. Identification of the congestion zones and congestion times, which is accomplished with the use of data extracted from google maps. This is one of the paid services utilized in this study
4. Preparation of pollution heatmap, which is accomplished by combining emissions and meteorology. This can be used for health exposure assessments and as simple as identifying representative locations for placing air quality monitors
5. Modeled source apportionment results, which is an output of the chemical transport modeling exercise, and this is the key ingredient for formulating a long-term action plan
6. The modeled data can also be used in conjunction with the available monitoring data to conduct short-term air quality forecasting
What kinds of resources are needed for such analyses and what technical skills do you need?
As I mentioned earlier, this is an involved process. There is some work on the emissions that can be accomplished using MS Excel. After that, the bulk of the analysis for spatial and temporal allocation of the emissions requires GIS software (which is free these days with packages like QGIS) and large computational space for running the meteorological (WRF) and chemical transport (CAMx) models, along with large storage.
On the technical skill set (a) familiarity with the needs of what data is necessary and in which format for building an emissions inventory (the need to get perfect information is what stops the process), (b) some familiarity with the emission sources (natural and anthropogenic) and their chemical nature (c) good knowledge of fortran/c programming which is still the main base for all the meteorological and chemical transport models, (d) some post-processing software programming like R/Python/Surfer/Matlab, (e) some core shell programming to handle the models and server management, and (f) good knowledge on how to use GIS software to manipulate large data feeds and customize information in a format ready for the models.
You’ve analyzed 50 cities around India now. What broad inferences can we draw?
The biggest inference is that we are never satisfied with the work and we are always looking for more information to learn and to improve. But, doing more is not an excuse to not act on what is already available and what we already know about the air pollution sources in the regions.
For most of the cities under the APnA program, this is the first time an emissions inventory and source-based apportionment was conducted at this spatial and temporal scales. The overall comparisons with limited monitoring data provide the much-needed confidence in taking this forward for (a) to further explore the possibilities of bettering the analysis with some customized local inputs and (b) using the results for formulating some long-term policy measures and analyzing the proposed activities, for example those proposed under the national clean air programme (NCAP) for some of these cities.
Doing the exercise for 50 cities over the last three years, provided a lot of learning on available datasets, how to customize the feeds, which information is useful and which is not, how best to optimize the operational scripts (like google maps speed information which is a paid service), and more importantly what the city looks like, where it is, and what is its pollution load.