AirDelhi: Fine-Grained Spatio-Temporal Particulate Matter Dataset From Delhi For ML based Modeling
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: PM2.5 Dataset
PM10 Dataset
Machine Learning (ML) Modeling
Mobile Sensing
Low Cost Sensing
TL;DR: Particulate Matter (PM) dataset collected from a highly polluted region, that has unique characteristics compared to existing datasets, opening new challenges of modeling and forecasting for ML researchers.

Abstract: Air pollution poses serious health concerns  in developing countries, such as India, necessitating large-scale measurement for correlation analysis, policy recommendations, and informed decision-making. However, fine-grained data collection is costly.  Specifically, static sensors for pollution measurement cost several thousand dollars per unit, leading to inadequate deployment and coverage. To complement the existing sparse static sensor network, we propose a mobile sensor network utilizing lower-cost PM2.5 sensors mounted on public buses in the Delhi-NCR region of India. Through this exercise, we introduce a novel dataset AirDelhi comprising PM2.5 and PM10 measurements. This dataset is made publicly available, at https://www.cse.iitd.ac.in/pollutiondata, serving as a valuable resource for machine learning (ML) researchers and environmentalists. We present three key contributions with the release of this dataset. Firstly, through in-depth statistical analysis, we demonstrate that the released dataset significantly differs from existing pollution datasets, highlighting its uniqueness and potential for new insights. Secondly, the dataset quality been validated against existing expensive sensors. Thirdly, we conduct a benchmarking exercise (https://github.com/sachin-iitd/DelhiPMDatasetBenchmark), evaluating state-of-the-art methods for interpolation, feature imputation, and forecasting on this dataset, which is the largest publicly available PM dataset to date. The results of the benchmarking exercise underscore the substantial disparities in accuracy between the proposed dataset and other publicly available datasets. This finding highlights the complexity and richness of our dataset, emphasizing its value for advancing research in the field of air pollution.

AIRDELHI: Fine-Grained Spatio-Temporal Particulate Matter Dataset From Delhi For ML based Modeling
Sachin Kumar Chauhan, Sayan Ranu, Rijurekha Sen Department of Computer Science
IIT Delhi {csz188012, sayanranu, riju)@cse. iitd. ac. in
Zeel B Patel, Nipun Batra
Department of Computer Science
IIT Gandhinagar (patel_zeed nipun. biita) ac.in
Abstract
Air pollution poses serious health concerns in developing countries, such as In- dia, necessitating large-scale measurement for correlation analysis, policy recom- mendations, and informed decision-making. However, fine-grained data collec- tion is costly. Specifically, static sensors for pollution measurement cost several thousand dollars per unit, leading to inadequate deployment and coverage. To complement the existing sparse static sensor network, we propose mobile sen- sor network utilizing lower-cost PM2.5 sensors mounted on public buses in the Delhi-NCR region India. Through this exercise, we introduce a novel dataset A AIRDEL.H comprising PM2.5 and PM measurements. This dataset is made pub- licly available at httppss: // www. ese. ittd. ac. in/ po wollluttinddd as valuable resource for machine learning (ML) researchers and environmental- ists. We present three key contributions with the release of this dataset. Firstly, through in-depth statistical analysis, demonstrate that the released dataset sig- nificantly differs from existing pollution datasets, highlighting its uniqueness and potential for new insights. Secondly, the dataset quality been validated against existing expensive sensors. Thirdly, we conduct benchmarking exercise (https: // g& thub. com/ sachin- it td/ PiDahhitttttttteeeaaaaaa uating statc-of-the-an methods for interpolation, feature imputation, and forecast- ing on this dataset, which is the largest publicly available PM dataset date. The results of the benchmarking exercise underscore the substantial disparities accuracy between the proposed dataset and other publicly available datasets. This finding highlights the complexity and richness of our dataset, emphasizing its value for advancing research in the field of air pollution.
serving
) eval-
Introduction
United Nations Sustainable Development Goals [UN]
2015]. especially the Goal-11 Sustainable
Cities and Communities, is primary research focus in institutions of developing countries like India (IITD (023), with related research works in sustainable transport [Chauhan al. 2020 and
pollution Shukka et al., 2020 Bikkina et al.) 2019: Iyer et al. 2022 Abidi et 2022].
Air pollution has now reached life-threatening levels in Delhi-National Capital Region (NCR), India (Tripathi et al.) 2019: Mannucci and Franchini 2017]. which is one of the most densely populated urban centers. The population of Delhi-NCR exceeds 46 million people [Nagar et al. 2017] and it has been reported that 50% of all children staying in this region suffer from inreversible lung damage [Chatterji 2021 ORF 2021]. Particulate Matter (PM) is especially dangerous, since
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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our breathing cannot filter out the ultra- fitra-fine particles. To mitigate the effects of air pollution. there is an urgent need to identify causes of pollution and strategies to curb its spread. [Sahu et al. 2020, Sutaria 2022] suggests to use one sensor per km2 for better pollution analysis. The Central Pollution Control Board (CPCB) and Delhi Pollution Control Committee (DPCC) have only 81 realtime air pollution measurement centers in Delhi-NCR Sutaria [2022 with 65 manually monitored centers,
which are thoroughly inadequate [Guttikunda et al. 2023 ET 2022 to cover the vast geography of [CCPPB 2018).
55, 000 km²
In the literature, several models have been proposed for predicting pollution levels at same/future time points (Patel et al.) 2022 Gao and Li 2021 Kurt et al., [0088] Tsai et al. 2018 Le et al. 2020].
and identifying factors affecting pollution [Atee et al.) [011]Googll 2014]] Messier et al. [0188]
Apte et a 2017 Alexeeff
[lexeel et al. 20188. There exists interpolation models [Qiao et al.[[2019 Ras and
Williams| 2005 Hamilton el al.) 2017 Patel et al.) 2022] to reliably predict pollution levels at unseen
locations based on a sufficient number of pre-installed sensors. These models can improve with fine-grained pollution data. The interpolation and forecasting models are supervised in nature and
hence can do better with more training data. Unfortunately, collecting pollution data using realtime centers is highly expensive as each instrument costs thousands of US Dollars.
In this work, we aim to mitigate the problem of lack of sufficient data in a cost-effective manner. We design a low-cost sensing mechanism (thoroughly compared in quality against high cost sensors) that allows us to collect PM data over a subset of the Delhi-NCR region at fine spatio-temporal granularity. The key highlights and contributions of our work are:
1. Quality dataset: As it is not cost-effective to repeat even the low cost sensors per km², we establish a low-cost vehicle-mounted PM sensing network and release the largest PM2.5 dataset from one of the most polluted regions in the world. This dataset is shown to be as good as the data collected from the few high-cost static-sensor deployed in the same region. As it is very challenging to collect such dataset in developing country due to constraints in infrastructure and government permissions, we document our data collection experience briefly in the paper. ($ (....
2. Unique dataset: This dataset complements the static sensor data available from the government deployed instruments in important ways. The static sensors are located at the top of high towers to get precise recordings of ambient pollution values, not affected by local sources. Our mobile sensors, on the other hand, are installed in the bus driver's cabin to measures the ground level pollution that daily commuters breathe in. We also perform thorough comparison with PM datasets available from other parts of the world and establish that the released dataset is unique in terms of scale and statistical characteristics. Hence, it can be of immense value to environmental think tanks. ($ (.3).
3. Utility for ML modeling: Through extensive benchmarking using sfate-of-the-ar Machine Learning (ML) algorithms, we demonstrate the utility of this new dataset for modeling problems using ML, like spatio-tempors interpolation, missing data imputation and forecasting, The dataset shown to be more challenging to model with ML algorithms, compared to previously available datasets, as Delhi has much higher variance in PM across space and time. This dataset, therefore opens opportunities for ML researchers for designing and benchmarking new ML algorithms, to reduce the interpolation, missing data imputation or forecasting errors. ($4).
2 Related Work
A primer for Air Pollution Monitoring is available at Urbanemissions 2023 J. Spatio-temporal (ST) interpolation involves predicting air quality at unmonitored locations in the past and/or present time using training data observed from the sensors during the past and present time. Zheng et al. [2013) developed a co-training- based approach for ST interpolation using PM2.s values captured every hour from ground stations of 4 cities in China which are converted to AQI (Air Quality Index), along with meteorological and traffic data. Cheng al. 2018 proposed an attention- based hybrid model involving LSTM and dense layers and et al. [2022 proposed a domain-inspired non-stationary Gaussian process model for ST interpolation which can also be used for ST forecasting. The two used 36 monitoring stations in Beijing with the collection time interval of 1 hour (with the latter additionally using London data), alongside meteorological data.
Missing data imputation problem can be considered variation of spatio-tempont interpolation where observations on the data. Models that work for ST interpolation can mostly be adapted readily for this problem.
cube are missing at random and we want to impute the missing
Spatio-temporal forecasting aims to predict air quality at particular location in future using the past and current data available at all the installed sensors. Kurt el al.) [2008) developed an online neural network based approach to predict air quality maximum 3 days ahead in time using year PM data
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for 1 region in Turkey. Zheng et al. 2015 develop and deploy a machine learning based air quality forecasting system with the Chinese Ministry of Environmental Protection. Yi et al. 2018 develop deep learning based approach to provide short-term and long-term air quality forecasts. The two used meteorological data along with pollution data generated every hour from 2,296 stations in 302 Chinese cities, and converted these concentrations into corresponding (individual) AQIS according to
Chinese AQI standards. Air quality forecasting was posed as a challenge in KDD2018, where Luo el
al. [2019] presented winning solution based on a combination of classical machine learning and
deep learning models using the provided data from stations in Beijing and London. [ao and Li| [202]] propose graph- based LSTM model for air quality forecasting and evaluate Northwest China hourly data from 32 china stations. All these prior arts utilize the static ground stations Air Quality data for the analysis, which enforces a restricted spatial coverage. They also use meteorological
data from the respective regions. (Bhattacharyya el al. 2022 provides a similar USA AQI dataset collected from Air Quality Open Data Platform.
There also have been studies on low cost sensors available in market for developed (EU) regions Karagulian et al. [2019]. Also, projects about installing low cost sensors at different roadside locations, like Schneider al. [2023] and Iyer et al. [2022). to complement the existing expensive static sensor network are done recently, but they kept the sensors at fixed locations. Vehicle mounted air pollution sensing has also been conducted [Apte al. 2011 Google, 2014; Apte et al. [017]
Alexeeff et al. [0188] Guo et al. 2016)] Adams and Corr 2019 Li et al. with certain limitations. Abidi er al. [2022] used similar low-cost sensors to analyze the relation of PM with static (green cover, buildup, commercial, residential) and dynamic (meteorological, traffic) factors, particularly at traffic intersections with odd-even policy in Delhi. As per Yi al. (2015): Pavani and Rao (2017), static sensors are better in data quality, endurance and temporal resolution, a low-cost static sensor is better in temporal resolution which can provide (static) pollution maps with better temporal resolution for the installed locations. But the effective static pollution maps require careful placement of sensor nodes in large quantity leading to resource wastage. Mobile sensors are better in mobility, geographic coverage, maintenance, cost-efficiency which can provide (dynamic) pollution maps with greater geographic coverage, with limitation of communication overheads and redundant sampling. In contrast, we are working with the PM data, collected with mobile sensors deployed on frequent bus routes, which is fine-gres grained and provides better spatio-temporal coverage, and our benchmarked models do not rely on meteorological factors.
3 Dataset Description
3.1 Dataset Collection Challenges
Creating the mobile PM dataset (as a replacement for low cost static PM dataset and high-cost ground station PM dataset) required us to design and implement our own embedded platform, choosing and calibrating appropriate sensors for maximum accuracy at low cost. The complete design is presented in Goyal et al. (2018). We opted to install our device in public buses, to utilize their pre-definedfixed and frequent routes of travel. Packaging was challenging to securely mount the instruments in the public buses, avoiding theft and ensuring enough ambient air to measure PM. Cellular connectivity was intermittent as the buses traversed the city, requiring us to augment real time data transfer when signal was present, with local storage to save data when signal strength dropped. Finally, getting permissions from different government entities to instrument the public bus fleet needed strict safety certifications that our devices do not interfere with the electrical and mechanical functioning of bus.
We mounted pollution tracking sensors on the permissible 13 public buses in Delhi for 3 months (Nov 1, 2020 to Jan 31, 2021), collaboration with Delhi Integrated Multimodal Transport System, after rigorous tests for automotive safety certification and appropriate permissions and letters of support from the Delhi Ministry of Transport and Delhi Pollution Control Committee (provided in Appendix [). The Covid' 19 restrictions were relaxed by this time, limiting to containment zones at local level only [MHA] 2020). So, the impact of the COVID' 19 on the collected dataset is expected to be minimal, if not none.
As discussed in Goyal et al. 20188) , the inside of our cade instrument comprising (a) PM sensor measuring PM2.5, PM10 and PM1, (b) GPS sensor to locate the bus, (c) 4G radio to communicate data from bus to server, (d) SD card for locally storing data when 4G signal is unavailable, (e) BME sensor [BME, 2023), a sensor especially developed for mobile applications and wearables, to record temperature and relative humidity and (f) nicro-controller to orchestrate the cons--sorr-coommnicce
software (See Fig. [T]. The mounting location the bus driver's cabin, next to two open windows to allow enough flow (Fig. Tc). Each bus commutes for
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(a) Measuring device
(h) Mounting location
Mounted device (d) Bus trajectories
Figure I: (a) Inside of our PM measuring IoT unit. (b) Mounting location in bus driver's cabin in non air- conditioned public bus (below the existing white box). (c) Mounted loT unit in the bus (below the existing white hox). (d) Govermment deployed static sensors installed in and around our bus trajectories, as location icons.
16-20 hours per day, and our instruments collect data at fine granularity of 20 samples per minute. Overall, the bus trajectories cover 559 km², along the main arterial roads in North-West, North, North-East and South-Eas Delhi (Fig. It). The dataset, having 3 pollution parameters: PM1 PM2.5 and 10 with 7 non-pollution parameters: latitude, longitude, time, deviceld, pressure, temperature, iitd. in it/poolltionnataa/ with proper documentation,
relative humidity, has been made available https://www: and httl::ttt////tt httg:::/ ace. htt.a//oowooccccoooooooooooooo.. under Creative Commons Attribution 4.0 International License [CC-by4 2013
3.2 Data Quality Analysis
plots PM2.5 values measured by two low cost PM sensors built by us (cost USD 30), and the same measured by industry grade reference instrument TSI DustTrak (cost USD 9500), while all three instruments are placed close to each other. The plot shows hours of the day along a-axis and sensed PM2.5 values along y-axis, for 10 sample days Jul 21-31, 2021. This is after the deployment of the low cost sensors in the buses is over, and the sensors have been brought back to the lab.
Fig. 2
Dustrak
Mobilel
Mobile2
PM2s diff
PM2.5
50
07-23
07-24
07 30
07-21
07-2 17-22
07-30
07-31
07
07
Hourly Time from 2021-07-21 14:00:00
Hourly Time from 2021-07-21 14:00:00
(a) Time-series comparison
(b) Time-serics difference (Dustrak vs Mobilel)
100
Dustrak
(Dust'Trak Mobilel)
Mobilel
Count
60
75
Mobile2
40
Count.
20
50
(Mobilet Mobile2)
25
Count
60
40
20
0
23
38
70
-2.5
0.0
2.5
5.0
7.5
PM2.5
PM2.5 mean difference for each hour (d) Histogram of differences
(c) Histogram of PM2.5 values (Dustrak vs Mobile)
Figure 2: (a) PM2.5 values measured by our low-cost mobile PM sensors (USD 30) vs. TSI DustTrak (USD 9500) between Jul 21-31, 2021. (b) Mean Difference and (d) Histogram of pointwise differences of PM2.s values measured by DustTrak and low cost mobile PM sensors. (c) Histogram of PM2.5 in the intervals on x-axis. The values almost identical.
Fig. 2b shows the difference of hourly mean PM2.5 between DustTrak and one mobile sensor, the mean difference is 6.16%. Also, Fig. 2c shows the histograms of hourly mean PM2.5 for the shown PM2.s intervals for the three devices. Fig. 2d|shows the histogram of difference of hourly mean PM2.5 between DustTrak and one mobile sensor, and two mobile sensors, for the same 10 days. Also, Fig, g Appendix Brrendix[sshww shows the similar mean and standard deviation between the 3 devices. While the cost gap between the instruments is huge, the gap between their sensed PM2.5 values, as seen in the plots. negligible. This pattern has been observed consistently by us and other researchers (Zheng et al.
2018 Cheng et al. 2014 Gao et al. 2015 Rai et al. 2017 Jiao et al. 2016 Zheng et al. 2019]

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We additionally compare the distribution of PM2.5 values recorded by our mobile sensors vs, those by the high-cost static sensors, deployed sparse locations by CPCB and DPCC in Delhi-NCR. Fig. (a.ffftt) shows hours of day along x-axis and average 2M2.5 for that hour, as measured by reference grade static monitors, with standard-deviation bars along y-axis. Fig. (Right) shows the same averaged over all bus mounted sensors. We select the static sensors that are within 1km of mobile sensor trajectory for each hour, and plot for 7 sample days. Fig. 3h reveal that both static and bus mounted sensors show similar PM distributions for each day, in spite of the difference in heights they have been installed at, and the difference PM measurement technique. We see this agreement for the entire 3 months deployment period. The agreement between low cost mobile sensors, and co-located high cost TSI Dusttrak, as well as reference grade static monitors, give us confidence to release the dataset to the research community.
Gold Standard Sensor
Our Low-cost Sensor
600
20.69
28.67
400
PM2.5
20.01
28.62
200
28.56
10
20
10
20
28.54
Hourly Time
Hourly Time
77.10
77:15
27:16
77:14
Long tude
(a) Static v/s Mobile Sensors Data
(b) Heat Map
Figure 3: (a) Distribution PM2.s collected by our low-cost sensor and gold standard sensor over 7 random days. The distributions are similar across the two sets of instruments. (b) Heat Map for all bus routes for Dec 15, 2020 (darker locations contain more samples).
Heat Map: During our analysis, we discovered variations in data availability across different timestamps and spatial locations. was evident that certain timestamps were not available at all spatial locations. Furthermore, some spatial locations, which were situated along routes with fewer bus visits throughout the day, exhibited limited temporal samples. As illustrated Fig. [3.. a typical day (Dec 15, 2020) demonstrated this pattern, where the outermost locations (depicted in light/pink color) contained samples from 4 hours duration within the 16.5 66.5 effective temporal window. Conversely, the darker/brown locations
the bottom right of the figure displayed higher number of samples, ranging from 14 to 16.5 hours. These locations are associated with common bus routes that connect with the depot.
3.3 Dataset Novelty
Tables I and summaaize the statistics of the dataset. While vehicle mounted air pollution sensing
has been conducted [Apte er al. [201T] Google 2014 Apte er al. 2017 Alexeeff et al.) [0018] Guo ef al. 2016 Adams and Corr] 2019 Li el al. 2012 our dataset is unique in characteristics and scale. Specifically, only two studies from Ontario, Canada [Adams and Corr 2019 and Zurich, Switzerland [Li et al. (012) have made their datasets publicly available. The Zurich dataset does not include PM values. Compared to the Canada dataset, our dataset is 1000 times larger and has a significantly different distribution of PM values (See Tables and
This is understandable as Delhi-NCR is air pollution hotspot, whereas Zurich and Ontario have negligible PM levels. We also compare our dataset with a recent USA AQI dataset |hhatacharyya el collected from Air Quality Open Data Platform. We also purchased Wind Speed (WS) data
al. 2022
Table I: Details of Delhi, India and Hamilton, Ontario, Canada and USA datascts.
Metric
Delhi-NCR
Canada
USA
Total area
559 km2
1138 km2
54 cities
Total samples
12,542,183 12,
46,080
35,596
Samples with PM2.5
12.154
35.134
Pollutants covered
PM, PMa.5 5 and PM10
CO. NO, NO2. SO,. Os.
CO. NO2, SO,, Os.
PM1 PMu. and PM1
PMz.s and PM10
Meteorological
Temp, RH, Pressure,
Temp, RH, Pressure, Dew,
Wind Speed Public bus 91
Wind Speed, Wind Gust OpenDataPllaffrm
Sensor source Monitoring days
Commercial van 114
668
5

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Table 2: Statistical comparison of PM values in Delhi, Canada and USA datasets.
Delhi-NCR
Canada
USA
Metric
PM
2M2.5
PM10
PM1
PM2.5
PM10
PM2.5 AQI
PM10 AQI
Mean
120.35
207.92
226.11
12.15
15.08
46.45
31.15
17.67
Std-dev
57.27
114.36
123.86
9.02
12.87
97.36
17.11
11.00
Missing %
0
0
71.71
73.62
72.24
1.30
52.34
for Nov 2020- Jan 2021 from www. windfinder. com complement our Delhi-NCR dataset for the meteorological analysis performed in Appendix[Eland Fig. [5] showing pollutants correlation with different meteorological factors and peculiar situations due to external factors (like impact of stubble burning episodes). Due to this behaviour, interpolation and forecasting are hard problems. Still, in Appendix DD and Fig. [4] we observed covariance among some spatial locations, and in Fig. I we observed some autocorrelation over the entire dataset. Similar trends were observed with the bus route analysis shown Appendix Hence we decided to formulate an interpolation problem and 24 hour forecasting problem to analyze the model performance, next.
4 ML Modeling Benchmarks
In this section, we benchmark the machine learning problems of (1) spatio-tempone interpolation, (2) spatio-temporal data imputation and (3) spatio-temporal forecasting the proposed, Canada and USA datasets. This benchmarking study serves two roles. First, allows us to compare the complexities of the three datasets beyond just statistical characterization. Secondly, spatio-tempont interpolations, data imputations, and forecasting methods are crucial for environmental research, policy-making, and individual decision-making. They empower various stakeholders gain comprehensive understanding of air pollution, proactively address potential increases in pollution levels, and make informed choices reduce personal exposure. In order to harness the full potential of spatio-temporal forecasting, interpolations, and data imputations, it is crucial to benchmark and evaluate the performance of algorithms designed tackle these problems.
4.1 Formulation of different ML Prediction Problems
(a) Spatio-temporal Interpolation: Given set visible/available locations with input features (latitude, longitude and time) and PM2.5 available for T+1 days, we wish to estimate PM2.5 for set of held-out locations for the T+1 day using the input features (latitude, longitude and time). This approach is compatible the scenario where we have data for some locations and we interpolation algorithms to know the PM2.5 values at new locations.
(b) Spatio-temporal Missing Data Imputation: Given set of locations with input features (latitude, longitude and time) and PM2.5 available for T days and a set of visible locations with input features (latitude, longitude and time) and PM2.5 available for the T+1 day, we wish to estimate PM2.s for a set of held-out locations for the T+1 day using the input features (latitude, longitude and time). This setting is compatible to the scenario where we have intermittent data missing throughout the day and we use interpolation algorithms to predict the missing points taking past & present data as input.
(c) Spatio-temporal Forecasting: Given a set of all locations with input features (latitude, longitude and time) and PM2.5 available for T days, we wish to estimate PM2.s for set of all locations for the T+1 th day using the input features (latitude, longitude and time).
4.2 ML Algorithms Benchmarked in this Paper
(a) Mean Predictor is the simple mean value of all visible samples which is used the value of all the held-out locations. The mean value of all visible PM2.5 locations C is used as the value of the held-out PM2.5 locations P.
PM's + mean Vp E P. where mean < + Σ Vc
(b) Inverse Distance Weighting (IDW) is the weighted average value of all visible samples in terms of distance, which is used as the value of the held-out P llocations.
PMB < Σ
PMC Ve E C' Vp E P. where F is linear function on distance d. F(d..)
(c) Random Forest (RF) is non-linear model capable of modeling complex spaces, It is known to perform efficiently on non-linear regression tasks, using an ensemble of multiple decision trees, taking the final output as the mean of the output from all trees.

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(d) XGBoost (XGB) iteratively combines the results from weak estimators. It uses gradient descent while adding new trees during training.
(e) ARIMA or Auto-Regressive Integrated Moving Average is a statistical time-series forecasting model that uses linear regression. It is configured using parameters (p, d, g) as: p is the number of lag observations included in the model, d is the number of times raw observations are differenced, and q is the size of the moving average window. We use ARIMA with parameters (3, 1, 1).
(f) N-BEATS is Neural Basis Expansion Analysis for Time Series, a deep learning model for zero-shot seme-ssries forecasting (Oreshkin et al. 2020]. We use the code from Python library "Darts".
(g) Non-Stationary Gaussian Process (NSGP) is a recent gaussian processes baseline [Patel er al. 20222] It learns a non-stationary covariance [Plagemano ef al. 2008 for latitude and longitude and locally periodic covariance for time. In general, Gaussian process Ras and Williams| (005 a.k.a Kriging is a Bayesian non-parametric model known as the best unbiased predictor in spatial interpolation domain. With only three tunable parameters, it is considered a strong baseline.
(h) Graphsage is a graph neural network model to learn and predict values at unknown spatio- temporal locations (Hamilton et al. 2017]. We transform the PM data a graph, and use Graphsage for interpolation and missing data imputation. Our graph formulation is available in Appendix A
4.3 Dataset Pre-processing and Evaluation Metrics for the Analysis
To benchmark interpolation and missing data interpolation ML modeling algorithms, we split the data into two parts for visible and held-out/hidden to use the held-out part for testing purpose. For forecasting based ML modeling, the visible/held-out split is not required, as predictions are made for future timestamps for all locations. For the Delhi dataset, we focus on the data collected from Nov 12, 2020, to Jan 30, 2021, excluding the initial days when there were fewer instruments on the buses and limited sample data. Additionally, 5:30 AM IST when buses remain stationary at confined bus-depots. To facilitate analysis, we divide the geographical area into square spatial grids with side length of km. These grids are further converted into spatio-temporal cells with a time interval of 30 minutes. To obtain representative PM values, we compute the average of all samples within each spatio-temporal cell. Subsequently, we employ K-fold cross-validation to partition the data into K PM2.s sets for each day, out of which I set is reserved for test and others used in training. The results obtained from the Delhi dataset are denoted as Delhi (Day) in the generated plots.
exclude the nightly data between 10 PM IST and
Additionally, utilize two open-sources PM2.5 datasets, from Hamilton in Ontario, Canada [Adams] and Corr, 2019] and from USA [Bhattacharyya et al., 2022]. For the Canada dataset, we process the data from 18 distinct days in the year 2015 using the same methodology. These results are presented as Canada (Day) in the respective experiments. As the data for Canada exhibits temporal sparsity. we project the data for each year onto single day and treat it equivalent to 11 days (from 2006 to 2016). The outcomes of this processing approach are depicted as Canada (Year) in the experiments. For the USA data, we use the available PM2.5 data across 54 cities from Jan 1, 2019 to Dec 11, 2020, and the results are presented as USA (Day). We benchmark the datasets on Nvidia DGX Workstation (with 4X Tesla V100 GPUs) and the benchmarking code is available at https: // g thub. com/ sachin it /d/ The PHD aase t.nt
Evaluation Metrics: The Loss is computed as:
N
RMSE(L, Lp)
1 N
Σ
( - yy)
where y/ is the predicted and y is the true PM2.5 value, and N is the total number of samples. For each of the K folds, we separately compute RMSE for that fold, and then plot average with standard deviation bars over the K folds. The lower RMSE being the better.
Notation:
We use T (consecutive) days data for the training and take the next day for test/evaluation. For interpolation benchmarking, Fig. 4a denotes the various subsets of this T+1 days data as A, B, C and P, for one fold of K = 5 fold validation. The is the set over which Prediction is to be done. the C set can be understood as the Context for prediction. For given fold, A is the visible set with the held-out set with the remaining 20% of the T train days data. A U B forms the whole dataset for the T train days. Similarly, C is the visible set with 80% of the test day data, P is the held-out set with the remaining 20% of the test day and C U P forms the whole dataset for the test day. The held-out locations B and P will be different for each of the K folds. The
80% of all T train days data,
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28.72
28.69
Deint
28.67
New Della
B
aattude
28.64
28.62
Lalitudo
Test Day
28.59
28.56
Train Days
Time
28.54
77.10
77.13
77.16 Longitude
77.19
Longitude
(a) An arbitrary split of A. and P sets.
(b) Visible (A) and Held-out (B) sets over map.
Figure 4: PM Data Splits. (a) A forms the whole dataset for the T train days, CUP forms the whole dataset for the test day. Spatial locations of C or P are independent of spatial locations of A or B. (b) Set of A and B spatial locations for 3 PM to 4:30 PM Dec 15, 2020 from all bus-routes.
A and C though both being visible sets, are independent of each other. The exact number of locations
in A, B. C and P change across the K folds. In Fig. 4bb we show set A and spatial locations in Delhi dataset for PM to 4:30 PM on Dec 15, 2020.
4.4
Observations and Inferences
shows the RMSE for interpolation, using 5-fold cross validation for the two training config- urations ACT in Fig. 5a and C in Fig. Fig. for 3 training days. ACT uses the visible set from both training and test days, while C uses only the test day's PM2.5 visible set. The missing data imputation plots are almost identical to the interpolation plots, so omit these for space constraints.
Fig. 5
MeanPred
MeanPred
60
IDW
150
IDW
RMSE
RF
RMS2
XGBoost
100
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Delhi (Day) Canuda (Day)
Canada (Year).
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(a) Train:ATT, InP,t:ACT, Predict:P
(b) Train:C, Input:C, Predict:P
Figure 5: Interpolation RMSE. Training days' data is used by ML model in (a) and not used in (b).
Observation 1: Delhi dataset is harder to model. All experiments over Delhi data show higher RMSE and all experiments over Canada and USA data show low RMSE, for both interpolation and forecasting, in Figures 5 and 7 This shows that Delhi data is more challenging for ML modeling, than the currently available PM datasets. Also, the majority of samples present in the data is below 800 PM2.5- Omitting the 29 test samples with higher PM2.5 for the purview of low-cost sensor accuracy, in Fig. 6 we see the MAE and RMSE for different PM2.5 levels for Random Forest (RF) algorithm. We observe that the modeling errors increase with increasing PM2.5 levels. For existing dataset like Canada with very low PM2.5 of mean 15 + 13 (refer the Table 2). the expected modeling error would automatically be low, making the data easier to model. For the Delhi dataset with high PM2.5 of mean 208 + 114, makes the dataset hard to model.
Observation 2: Learning from data helps in modeling the Delhi dataset. All ML based algorithms show significant improvement over Mean Predictor for Delhi data in Figures whereas improvement for Canada and USA data over Mean Predictor is not significant. In Fig. 5p. all ML algorithms exhibit less than 40 RMSE while Mean Predictor RMSE is 65.80 for Delhi data (best case improvement is 66.2% for RF and worst case 39.3% for IDW). For Canada data, best case improvement and worst sees improvement, whereas for USA AQI data, improvement is within 16% 26%.
27%
Observation 3: Traditional MI algorithms as well as the recent models for the Delhi dataset. Learning from data matters, the ML based models do better than the mean predictor. But the recent
8

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250
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Figure 6: Errors for different PM2.s levels (at intervals of 10) for RF algorithm, with the number of test samples. Modeling shows high emror at high PM2.5. making Delhi dataset hard to model due to high PM2.5 levels.
complex Bayesian models like NSGP, and the neural network based models like GraphSage (for interpolation) and N-BEATS (for forecasting), do not outperform powerful traditional ML models like Random Forest. For instance, RF performs best for interpolation (RMSE 22.24 in Fig. and XGBoost performs best for forecasting (RMSE 84. 15 in Fig. 7
Observation 4: Historical training data adds value for interpolation. For the spatio-temporal interpolation problem, just using data from the visible set C from test day is enough to predict the held-out P data with low RMSE. For example, the RMSE for RF is similar (22.24) for test day only data C' in Fig. 5p and with including train day data ACT in Fig. 5p. And XGBoost is better for C with RMSE 29.73 than for ACT with RMSE 33.24. NSGP is the only algorithm, which sees huge jump in RMSE when not using training data from past days. Thus PM for a given day is mostly unrelated to PM on past days, and using historical training data has no significant impact on interpolation RMSE.
Observation 5: A location's 's air quality related to nearby location. In Fig- 5 for Delhi data, we observe that IDW performs significantly better than the Mean Predictor. Mean Predictor does not take the distance into account, whereas the IDW gives more weightage to nearby locations data. So, the impact of an adjacent location is significant for IDW w.r.t Mean Predictor, pointing that nearby locations air quality impacts the air quality of a location.
Fig. 7 shows RMSE of forecasting. Graphsage does not work in this setting as it requires subset of test day's 's data for edge formation to the data being predicted. So we drop Graphsage, and add two forecasting specific baselines: ARIMA and N-BEATS, that are not suitable for interpolation.
Observation 6: Forecasting is a harder problem than interpolation. Forecasting RMSEs are
significantly higher than interpolation RMSEs. The best model in forecasting is XGBoost in Fig. with RMSE 84. 84.15, whereas the best model for interpolation
Fig. Sjis RF with RMSE 22.24. Higher forecasting RMSE compared to interpolation also supports that previous day's 's data has less impact on test day's PM data. Hence forecasting using only past days' data for an unseen future test day is hard.
Observation 7: How time is normalized affects forecasting accuracy. In Fig. 7a, time normal- till last train
ization done across days, i.c. time starts on first train day and increases day. ARIMA/ / N- don't 't normalize the time directly, they take all PM values in a sequence corresponding time from start end. RF/XGBoost takes input in random sequence and hence takes the time as a state parameter, which can be normalized from start to end, or for each day. Table 7p compares this time normalization across days (T), normalizing separately for each day. RF, XGBoost and NSGP show lower RMME for separate normalization for each day, while IDW does better with normalization across days. This pre- pe-processing step of time normalization therefore should be carefully decided based on the ML algorithm.
150
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ABT
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106.41
Figure 7: Forecasting RMSE, (a) Normalization is done across all days. (b) Comparison of normalization across
all days (T) vs normalization over each day.

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Limitations and Future Work
We acknowledge this dataset is limited to 3 months, and we are working to scale in another metropoli- tan city in India for comprehensive 6+ months data collection and analysis. The bus routes selected by us covered the different types of areas, including green cover, residential, commercial etc, termed as static factors. Hence we have tried our best to reduce the bias towards any particular factor. Still, there limited spatial distribution due to the bus routes over urban arterial roads. An alternative could have been to put sensors in private vehicles, but it is not reasonable to drive cars just to gather the data. Also, commercial cabs can used for data collection, which we can check in our future work. We anyhow moving from high limitation of few data points in the same vicinity to million+ points.
In Appendix G we also discuss anomaly detection used to fix any faults in the low cost sensors. We check for samples recorded per minute, number of minutes each device is active in an hour, number of active hours in day, samples recorded per region, inter-sensor PM values variation, and intra-sensor PM values variation, during dataset collection to effectively keep validating the data being collected.
In our future work, aim to address the problem of recommending suitable locations for installing new expensive sensors effectively within budget constraints, a challenging task in a developing country like India. By leveraging the insights gained from this research, we strive to optimize the allocation of resources and enhance the efficiency of the monitoring network, further strengthening pollution mitigation efforts.
Conclusion
Delhi-NCR, with its notorious air pollution problem, poses significant health risk to its population of approximately 46 million individuals. In this paper, we present a novel PM dataset AIR DELHI collected from this region using low-cost IoT devices deployed on public buses. This dataset serves as a valuable resource for environmental researchers and medical practitioners, offering insights into ground-level PM exposure for daily commuters and temporal variations in PM levels over days and weeks. provides a comprehensive view of spatial variations across different locations within the region.
Through thorough statistical amalysis and benchmarking studies, we have established that the released dataset is distinct from any other existing pollution dataset. By comparing the performance of machine learning algorithms on the released dataset against the Canada and USA datasets, we have demonstrated the significant differences in characteristics and challenges associated with the Delhi- NCR dataset. This highlights the need for specialized approaches and tailored solutions to address the unique complexities of air pollution in this region.
The low-cost mobile data collection has the potential to complement the expensive static sensor network in the city, empowering citizens to make informed decisions regarding local PM levels. This includes determining the safety of engaging in outdoor activities, choosing appropriate protective measures such as face-masks air purifiers, and selecting optimal commuting routes and transporta- tion modes to minimize PM exposure. Such considerations are vital for safeguarding public health and promoting environmental sustainability.
To foster further advancements in the field of envirommental sustainability, we release both the code and data associated with this study. This allows researchers to build upon work, explore new avenues of inquiry, and contribute to the collective understanding and management air pollution-related challenges.
Acknowledgments and Disclosure of Funding
This work was supported by DST-SERB for funding through IMPRINT-II research grant. We also thank the Delhi Integrated Multimodal Transit System (DIMTS) for allowing us to instrument their fleet.
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14


Reviews:
[{"review_id": "VmzcQDynxH", "reviewer": "Reviewer_5SWu", "summary": "", "questions": "", "limitations": "The limitations should be discussed, especially in terms of where the sensors made mistakes that can be detected by comparing to other data sources.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. The released dataset is interesting as it is a new mobile air quality data set from a region susceptible to air pollution episodes. \n2. The work on the mobile air quality benchmark is a good starting point for a serious air quality model benchmark.\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "5AADVf2uOU", "reviewer": "Reviewer_npPd", "summary": "", "questions": "", "limitations": "- The lack of detailed information on the specific sensor selection process and calibration methods employed raises questions about the accuracy and reliability of the measurements obtained from the sensors. \n- The paper does not address the potential impact of intermittent cellular connectivity on data completeness and accuracy. It is important to acknowledge and discuss any potential data loss or handling methods related to intermittent connectivity during bus travel. \n- The paper does not explicitly acknowledge or discuss the potential biases introduced by focusing on specific bus routes/areas for data collection. This may limit the generalizability of the model, and it would be beneficial for the authors to address and discuss any biases or limitations associated with the selected bus routes/areas. ", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "\n- Provision of publicly accessible PM2.5 and PM10 dataset for air pollution analysis \n- Provision of benchmarking exercise, evaluating state-of-the-art methods for interpolation, feature imputation, and forecasting on this dataset ", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "nh7jenoMOB", "reviewer": "Reviewer_Q5NU", "summary": "", "questions": "", "limitations": "While this work does not raise explicit potential negative societal implications, there are several limitations that deserve to be discussed.\n\nPerhaps it warrants a mention that the dataset is collected during COVID time under limitations.\n\n\nSome discussion on the uneven spatial distribution of the dataset (i.e., which is constrained by the bus routes) should be included, e.g., under limitations.\n\nThe dataset covers a period of 3 months, which is another potential limitation to mention.", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "The paper is very well-motivated and it addresses a pressing real-world challenge. For example, in Intro, it is stated that 50% of all children in the Delhi region suffer from irreversible lung damage. \n\nThe paper also very well discusses its main implications. For example, it states in the Conclusion that the aim is to recommend suitable locations for installing new expensive stationary sensors. Also, the method could be used to recommend to people which commuting/travel routes to take and when to wear masks.\n\nThe contributions are mainly well-stated.\n\nThe frequency at which samples are collected.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "HhrcFCB6bj", "reviewer": "Reviewer_8Gda", "summary": "", "questions": "", "limitations": "The variance in the data due to noisy low-cost sensors and the problem of calibration needs to be discussed. Further, the impact of the position of the sensor box inside the bus, as opposed to ambient pollution sensors that are not within a vehicle needs to be analyzed.", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "* The findings of how the dataset varies from previous dynamic sensing networks is valuable for future work studying pollution in highly-polluted cities like Delhi\n* ML interpolation and forecasting benchmarks shows that traditional ML methods are sufficient to learn the patterns in the data\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: We seldom come across datasets where the data creators capture the data themselves rather than extract them from some system designed by a different team. This is an example of what Kiri Wagstaff referred to as "machine learning that matters". 

The reviewers generally pointed out that the paper is well-motivated, addressing a pressing real-world challenge, the execution is well-done, and the work is impactful. Furthermore, the mobile air quality benchmark can be used as a starting point for future benchmarks

There was one reviewer who did not have a positive view of the work, but they did not revise their review after the author rebuttal, and therefore their review was weighted less.

Overall, I recommend acceptance.
Decision: Accept (Poster)