1. Air Quality Management
1.1 An Overview
Hyperlocal Air Quality measurement is the steppingstone for creating a decision support system for a sustainable city considering the evolving nature of rapid urbanization. Strategically positioned sensors in various microenvironments can provide precise information on air quality and the leading sources of pollution, helping prioritize developmental decisions for the stakeholders.
At Aurassure we believe that distributed networks of small-scale-low-cost environmental sensors enable an entirely new approach to understanding our environment. Our state-of-the-art technology is an end-to-end solution consisting of sensors, hardware, and a cloud platform to help monitor air quality. Our platform transforms the hyperlocal air quality data captured from a pool of distributed sensors into meaningful insights, thus raising awareness and providing crucial information at the right moment to help improve resilience. “With Aurassure we help you see the unseen to secure your future.”
2. Air Quality Sensor Network in Bhubaneswar
2.1 Study SIte and Study Period
The “Air Quality Green Field Project” experiment was conducted in the city of Bhubaneswar, India. The goal was to develop a hyper-local air quality monitoring network. This paper describes the network setup process and lessons learnt. It also presents some very early results from the first 6 months of data collection (July - December 2021).
2.2 About-Bhubaneswar
Bhubaneswar, a mid-sized city fondly known as the temple city of India, offers a unique blend of antiquity and modernity. Bhubaneswar was one of the first planned cities of India, with an abundance of greenery. As the capital of Odisha, it showed remarkable expansion both in terms of population and area. The city now has a diverse population, vibrant culture, rapidly growing economy, and is an emerging sports capital of the country.
The rapid increase in urbanization and exploding rise in vehicular population has resulted in a rapid increase in the built-up land and a reduction in green cover in and around the city. This is responsible for the deteriorating of air quality in the city. Monitoring, analysing, and identifying the sources of air pollution has become necessary for an effective response to its rising levels.
2.3 Stakeholder Consultation and Identification
During the planning and execution of this “green field air quality project” we consulted with key stakeholders and government bodies in the city including:
- Capital Region Urban Transit System (CRUT)
- STPI - Bhubaneswar - Software Technology Parks of India
2.4 Issues Identified
As per the reports by the Odisha State Pollution Control Board, Bhubaneswar records a high level of suspended particulate matter (SPM), thereby increasing the health risks including the cardio- respiratory diseases.
The city only has a few monitoring stations, placed at a far-off distance from Bhubaneswar nearly 150 - 200 km away, as per the data available on https://app.cpcbccr.com/AQI_India/.
These sparse network of devices results in significant data gaps when it comes to understanding air quality at the local level.
We approached the officials at, STPI-Bhubaneswar (Software Technology Parks of India- Bhubaneswar), CRUT (Capital Region Urban Transport) and proposed a pan-city “Air Quality Green Field Project” that included the deployment of 50 environmental monitoring sensors measuring key parameters responsible for Air Quality Index.
The officials willingly accepted and supported our proposal and gave the necessary permissions for the deployment of a hyperlocal air quality monitoring network in the city.
In partnership with Google LLC , we could carry out the green field air quality project successfully in Bhubaneswar city to build a dense hyperlocal air quality monitoring network. The vision is to use this case study as a framework for learning and deploying similar projects in every other city across India and Overseas.
3. Groundwork
3.1 Selection of locations for static sensor deployment
Sensor Deployment Map
Bhubaneswar being a Tier 2 city with more than 8.4 Lakh population and varieties of demographics with an urban area coverage of 186 km².
(https://www.bmc.gov.in/about/profile) under the civic administrative body, we must carefully select the deployment location and grid size for this study.
The city was divided using a uniform 5 Sq. Km grid to study the spatial variations in the emission and the pollution loads. Among the 40 grids, the domain was further categorized into zones such as residential, industrial, construction, commercial, traffic junctions, forests, water bodies, etc.
The air pollution situation in urban areas is highly related to human activities, which majorly contributes to our careful selection of the deployment locations. In this deployment strategy we tried to capture most dynamic areas covering higher population density and major traffic junctions which might be contributing significantly to the air pollution levels. Other areas such as forest lands, water bodies and less population density areas have been given lesser priorities for sensor deployment.
In all the selected locations we further selected sensor sites with good airflow from all directions. A post installation analysis was also done to verify the viability of the locations from a geospatial point of view. This deployment strategy ensured good location selection for the static sensor deployments.
3.2 Selection of vehicles/partners for mobile sensor deployment
The main aim of deploying mobile sensors across the city was to monitor key air quality parameters in different areas of the city dynamically. To do this, we placed our mobile sensors on auto rickshaws and other public transport vehicles. We chose public vehicles because public transport follows the same route throughout the day, thus providing predictable and repeated coverage of city streets. Private vehicles do not commute regularly and without any guarantee of good coverage.
4. Execution
4.1 Pilot deployment of sensors
The pilot phase aimed to test the concept and to identify the shortcomings. A total of 5 air quality sensors were installed at both static locations and on vehicles to monitor key pollutants in near real-time. Data gathered during this pilot phase was analysed to determine the feasibility of the proposed monitoring system. With the learning from pilot, we scaled up to 50 sensors across the city. During the pilot phase we found that the auto rickshaw was not the perfect fit for mobile sensor deployment. The challenges of installation in auto rickshaws led us to switch to installing mobile sensors on buses of the Capital Region Urban Transit System (CRUT).
4.2 Deployment of static air quality sensors and challenges faced
Site surveys were conducted to identify the ideal locations for sensor deployment. Surveys confirmed that the chosen locations meet the basic requirements for successful installation, integration, and operation of air quality monitors. We considered safety, network coverage, availability of power sources, proper airflow, and lack of disturbances.
During the deployment we faced challenges at private owned locations. Owners were suspicious of hidden surveillance devices in the platforms. Many expected a monthly pay-out for the installation just like any commercial billboards would provide. Most were not ready to provide any connection to power sources fearing short circuits. This added to the project overhead cost. Safety audits at some government owned sites added to the project execution timeline. Multiple ownership of infrastructure at some locations required dual permissions from the authorities. However, officials were cooperative and friendly, and all permissions were granted in a timely manner.
The installation was completed successfully within the allotted time frame. The monitoring devices being compact, and lightweight were easily installed to the existing infrastructure such as building walls and traffic poles. Meanwhile, all the necessary safety precautions were taken care of.
4.3 Deployment of mobile air quality sensors and challenges faced
We initially planned to use the most common public transport, auto rickshaws, for the sensor installation. Being open they would allow us to map the pollutants in the immediate breathable air and the installation and permission process were easier. But the challenges were:
- The path followed by autorickshaw was not uniform, and in some cases the rickshaw wentoutside the study area and hence data on that day was lost.
- The safety of devices was also a major concern on these vehicles and the prices chargedby operators added an extra overhead cost.
We therefore had to move to a more reliable and consistent moving platform for the device installation and chose to use Mo Bus public buses. This overcame all the shortcomings of the auto rickshaws and provided several advantages:
- The buses follow fixed routes
- Buses run on regular predefined schedules
- Allowed bulk installation and maintenance
- Creates greater public awareness
The sensors were installed inside the buses in a location that ensured optimal airflow so that the sensor can measure air representative of the ambient atmosphere that people are breathing.
4.4 Technical Architecture
The sensor network measures air quality in real time and sends the data to Google Cloud Platform. Wireless mobile network technology is used to communicate between the sensors and the IoT cloud platform for data transmission. To make the information relevant, the platform analyses and processes all the data it receives. This data is then provided to stakeholders and citizens via a real-time dashboard, which displays the data using interactive graphics and statistics for easy comprehension. The dashboard allows the user to follow and visualize the status of any location of their city remotely at their comfort, allowing them to make informed and intelligent decisions based on the information available.
4.5 Maintenance of networks-Regular calibration and sensor replacements
Periodic calibration of sensors was carried out to ensure the data is continuously adjusted to be as accurate as possible for the local conditions. We relied on algorithms based on machine learning for improving the generality of calibration and reducing manual calibration process. The sensors are brought in proximity to a reference device. QA checks and descriptive statistics are carried out to evaluate the behaviour of sensors and the reference device. Accordingly, the sensors are trained to correct the errors and drifts using weather measurements and other information sources.
Error codes, data availability percentage and the performance of the power source were also monitored regularly. Trackers were used to gauge the signal strength and the data pre- processing time at the sensor end. In the case of mobile sensors, we connected with the bus authorities for information on change in bus routes, maintenance downtime and any other device safety issue.
Depending on the site and sensor readings, the offset and multiplier was adjusted on a regular basis. If the sensor goes offline, or starts giving higher values than normal, alerts were generated to allow the team to take timely action and resolve the issue.
5. Impact
5.1 Hyperlocal Air Quality Sensor Network in Bhubaneswar
We successfully created the hybrid hyperlocal network of air quality sensors in Bhubaneswar, in collaboration with CRUT (Capital Region Urban Transport) and the STPI - Bhubaneswar (Software Technology Parks of India). This will now serve as a blueprint for planning air quality monitoring networks in other cities.
Currently, our network consists of 50 air quality sensors in both static and mobile locations collecting air quality data every minute of the day at various locations across the city. All the sensors are using 4G LTE to transmit data to the cloud server. The sensors are measuring airborne pollutants, including PM2.5, PM10, CO2, VOCs, SO2, NO2, and temperature and humidity.
“Collecting over a hundred thousand data points daily.
This dense network has helped us collect over 20 million data points over a period of 180 days’ time with high spatial and temporal resolution. The data sheds light on actual sources of pollution, and we are now able to see where and when the pollution spikes. The network was also able to identify new pollution hotspots, such as a bus garage which had not been identified by previous monitoring and modelling efforts. This shows how traffic and local activity are impacting the neighborhood air quality at specific locations, allowing residents, policy makers and the community to demand action to address the problem.
5.2 Key Metrices
Key metrices generated till date:
- Total AQ measurements till date – Approx. 13 million Data Points over 250
- days Total KMs driven for data collection - Approx. 1 million Km driven over 250 days
for more than 250 days
AQ measurements
driven to collect AQ data
Our intuitive software platform puts a tool into the hands of citizens who can rely on accurate information about the air in their surroundings. The Aurassure software platform with real- time dashboards provide users with direct access to insightful and actionable information at-a-glance, so they can make informed decisions and act quickly. Reports can be generated and shared easily between users. Our hyperlocal network’s greatest benefit is that we provide citizens with 'hyperlocal' tailored data, allowing them to make informed decisions about when and where to travel, how to travel, where to exercise, and even where to go to school or buy a home.
6. Learnings
6.1 Stakeholders Participation and Alignment
Based on our experience we recommend future project owners to collaborate with the local governing bodies to get permissions granted quickly. Also, we would recommend following a top-down approach for seeking permissions as this would ensure lower compliance requirements overall. We also learned that a simplified safety protocol document adhering to the local authority guidelines and catering to non-technical presentations is essential.
For the deployment of sensors, ensure that the location has adequate free space for airflow, and is safe from theft or tampering with the devices. Ensure availability of continuous power and network connectivity throughout the day for continuous data collection.
Based on our learnings, we observed that public modes of transport are the best for deploying mobile sensors. This is because they often follow a specified path, ensuring high data acquisition. We also need to increase the diversity of nature of sensor selection and ensure that the mobile sensors pass the stationary sensor locations periodically. This will help in deriving more actionable user insights.
Using public transportation for sensor deployment helps limit carbon footprints by leveraging vehicles already operating in areas of interest.
7. Conclusion and Next Steps
The impact of our solutions will be directed towards the city municipalities, environmental consultants, and government bodies to choose the best planning solution according to the quality life standards pursued by local governments. The air quality data will serve as a base for assessing the potential and impact of a city's clean air initiatives, thus helping drive effective environmental policies that will improve the city’s air quality, environment, and health of citizens. This will bring a drastic change in the regulation and decision-making system, which will be information driven and clear in the eyes of the citizens as well.
We also invite research scholars and institutes to join us in analysing the city's air quality data to identify and track potential exposures and evaluate health impacts, prioritize emission sources as potential targets for risk reduction activities, and assist health practitioners and city administration in identifying vulnerable populations and making data-driven decisions to mitigate air pollution. The Government bodies and communities across the world can also reach out to us if they want to deploy a similar kind of low-cost air quality monitoring network in their city, to ensure their citizens breathe cleaner air and promote a healthier environment.
8. Future Plans
8.1 Deep data analysis of AQ data in collaboration with research partners and government bodies
The data generated over time can be shared with all the stakeholders, decision makers and scientists to devise counter strategies. We can also process historical and continuous data collected over the time to compute AQI for the region and forecast its values using AI and ML. Individuals, communities and governments will be better able to identify access and manage health hazards caused by environmental conditions at the precise areas where they occur if scientists and citizens have access to the data.
In the next stage, we will also include a mobile app to allow citizens to monitor the air quality in their area. We also plan to send daily emails to citizens, using predictive data analysis, informing them of where and when pollution hotspots may occur that day, allowing them to change their schedules accordingly.
Cleaner air for Bhubaneswar will also improve the city's future and the wellbeing of its citizens. Clean air will make the city more attractive for citizens and visitors to live, work and play in. Being India's sports capital, frequent sporting events could serve as a catalyst for improving air quality.
8.2 Expanding the similar type of sensor network in other cities.
This project is unique in that it brings engagement from different varieties of stakeholders such as government, schools, corporate sponsors, and sensor manufacturers to create awareness and contribute towards mitigating the urban air pollution challenge. The success of this green field air quality project in Bhubaneswar will act as the blueprint for scaling up similar projects in various other cities of India and across the globe.
9. Acknowledgements
First of all, we would like to thank our technology partner and sponsor for the “Hyperlocal Air Quality Monitoring Network for Bhubaneswar ” project , Google LLC without which the project would not have been successful . Further , the support of CRUT -Capital Region Urban Transport and STPI-Bhubaneswar proved to be immensely helpful who facilitated the idea, and provided all possible support, and turned this pilot experiment into a huge success . Also , it is impossible to overlook the support and cooperation of communities , and private homeowners for their valuable assistance.
Finally, we appreciate all Aurassure team members' hard work and efforts in completing the project successfully. We take this opportunity to thank all the stakeholders of this project who made this achievement a success with their support and guidance.