Air pollution is a leading global health threat, yet many developing countries lack the dense monitoring infrastructure needed for accurate exposure assessment and informed policy. Optimal Sensor Placement (OSP) is a foundational challenge in expanding monitoring capacity. While mutual information (MI) offers a principled criterion for selecting informative sensor locations, its computational cost grows with both the number of placements and the density of the candidate grid. We present a scalable, continuous optimization framework that treats sensor coordinates as differentiable parameters and directly maximizes MI. Unlike standard approaches, our method is computationally efficient—its runtime is independent of both the number of placements and the size of the search grid—making MI-based acquisition feasible over large spatial domains. On a continental-scale PM2.5 dataset, our method outperforms random placement and the widely-used Maximum Predictive Variance heuristic. In a focused regional study, it approaches the performance of greedy MI while being orders of magnitude faster. Our framework enables practical, information-theoretic sensor placement for real-world environmental monitoring.

Air pollution was responsible for 2.6 million deaths across South Asia in 2021 alone, with brick manufacturing contributing significantly to this burden. In particular, the Indo-Gangetic Plain; a densely populated and highly polluted region spanning northern India, Pakistan, Bangladesh, and parts of Afghanistan sees brick kilns contributing 8–14% of ambient air pollution. Traditional monitoring approaches, such as field surveys and manual annotation using tools like Google Earth Pro, are time and labor-intensive. Prior ML-based efforts for automated detection have relied on costly high-resolution commercial imagery and non-public datasets, limiting reproducibility and scalability. In this work, we introduce SENTINELKILNDB, a publicly available, hand-validated benchmark of 62,671 brick kilns spanning threekiln types Fixed Chimney Bull’s Trench Kiln (FCBK), Circular FCBK (CFCBK), and Zigzag kilns - annotated with oriented bounding boxes (OBBs) across 2.8 million km2 using free and globally accessible Sentinel-2 imagery. We benchmark state-of-the-art oriented object detection models and evaluate generalization across in-region, out-of-region, and super-resolution settings. SENTINELKILNDB enables rigorous evaluation of geospatial generalization and robustness for low-resolution object detection, and provides a new testbed for ML models addressing real-world environmental and remote sensing challenges at a continental scale. Datasets and code are available in SentinelKilnDB Dataset and SentinelKilnDB Bench-mark, under the Creative Commons Attribution–NonCommercial 4.0 International License.

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.

Air pollution is a global problem and severely impacts human health. Fine-grained air quality (AQ) monitoring is important in mitigating air pollution. However, existing AQ station deployments are sparse. Conventional interpolation techniques fail to learn the complex AQ phenomena. Physics-based models require domain knowledge and pollution source data for AQ modeling. In this work, we propose a Gaussian processes based approach for estimating AQ. The important features of our approach are: a) a non-stationary (NS) kernel to allow input depended smoothness of fit; b) a Hamming distance-based kernel for categorical features; and c) a locally periodic kernel to capture temporal periodicity. We leverage batch-wise training to scale our approach to a large amount of data. Our approach outperforms the conventional baselines and a state-of-the-art neural attention-based approach.

Air pollution killed 1.67M people in India in 2019. Previous work has shown that accurate public perception can help people identify the health risks of air pollution and act accordingly. News media influence how the public defines a social problem. However, news media analysis on air pollution has been on a small scale and regional. In this work, we gauge print news media response to air pollution in India on a larger scale. We curated a dataset of 17.4K news articles on air pollution from two leading English daily newspapers spanning 11 years. We performed exploratory data analysis and topic modeling to reveal the news media response to air pollution. Our study shows that, although air pollution is a year-long problem in India, the news media limelight on the issue is periodic (temporal bias). News media prefer to focus on the air pollution issue of metropolitan cities rather than the cities which are worst hit by air pollution (geographical bias). Also, the air pollution source contributions discussed in news articles significantly deviate from the scientific studies. Finally, we analyze the challenges raised by our findings and suggest potential solutions as well as the policy implications of our work.

Air pollution is a global challenge for cities across the globe. Understanding the public perception of air pollution can help policymakers engage better with the public and appropriately introduce policies. Accurate public perception can also help people to identify the health risks of air pollution and act accordingly. Unfortunately, current techniques for determining perception are not scalable: it involves surveying few hundred people with questionnaire-based surveys. Using the advances in natural language processing (NLP), we propose a more scalable solution called Vartalaap to gauge public perception of air pollution via the microblogging social network Twitter. We curated a dataset of more than 1.2M tweets discussing Delhi-specific air pollution. We find that (unfortunately) the public is supportive of unproven mitigation strategies to reduce pollution, thus risking their health due to a false sense of security. We also find that air quality is a year-long problem, but the discussions are not proportional to the level of pollution and spike up when pollution is more visible. The information required by Vartalaap is publicly available and, as such, it can be immediately applied to study different societal issues across the world.

Sensing is central to the SenSys and related communities. However, fine-grained spatial sensing remains a challenge despite recent advancements, owing to cost, maintenance, among other factors. Thus, estimating the sensed phenomenon at unmonitored locations and strategically installing sensors is of prime importance. In this work, we introduce Polire - an open-source tool that provides a suite of algorithms for spatial interpolation and near-field passive sensor placements. We replicate two existing papers on these two tasks to show the efficacy of Polire. We believe that Polire is an essential step towards lowering entry barriers towards sensing and scientific reproducibility.