Air pollution is a significant public concern worldwide, and accurate data‐driven air pollution prediction is crucial for developing alerting systems and making urban decisions. As more and more cities establish their monitoring networks, there is a pressing need for coldstart model training with limited data accumulation in new cities. However, traditional spatial‐temporal modeling and transfer learning schemes have been challenged under this scenario because of insufficient usage of available source data and suboptimal transferring strategy. To address these issues, we propose a meta‐learning‐based spatial‐temporal adaptation solution for coldstart air pollution prediction. Our approach is a model‐agnostic framework that enables a given backbone predictor with adaption ability across different space and time locations. Specifically, it learns a factorization of the available source data distribution and recognizes the target city as one of its components, greatly reducing the data accumulation requirement and providing coldstart capability. Furthermore, we design a novel bidirectional meta‐learner that can simultaneously leverage task embeddings learned from data and features constructed based on prior knowledge. We conduct comprehensive experiments on both synthetic and real‐world air pollution datasets of four distinct pollutants. The results demonstrate that our proposed method achieves a 5.2% lower 24‐hour prediction mean absolute error (MAE) than pretraining and fine‐tuning solutions when facing a new city with only 200 hours of data, which empirically verifies the effectiveness of our approach as a coldstart training solution.

In order to reduce the measurement error of low cost sensor in the real-time mobile sensing network, rendezvous calibration mechanism is widely used. To tackle the sparsity of reference data and the lack of calibration opportunities, we propose ST-ICM: a Spatial-Temporal Inference Calibration Model based on Gaussian Process Regression, assisting the calibration task by creating more calibration grids in both spatial and temporal dimensions. By using the GPR, the inferred grids generated by ST-ICM are associated with various confidence levels. Based on this property, we propose to make use of a hyperparameter, i.e., variance threshold, to balance the tradeoff between the quantity and quality of the inferred grids. Specifically, only the grids with variances below the threshold will be employed. We conducted experiments using a real-world dataset collected in Nanjing, China, to evaluate the performance of the proposed ST-ICM. The experimenal results show that our model achieves 24% improvement on error calibration compared to the baseline.

Air pollution problem has caught much attention globally. In addition to the national air quality monitoring stations deployed by the government, the number of low-cost air quality sensors increases rapidly as a supplement to support fine-grained monitoring. In-field calibration methods are necessary for these low-cost sensor nodes to assure the data quality. However, it is costly to collect enough reference data after deployment to train the in-field calibration model and many sensors even have no synchronized reference in the real application scenarios. To address the above challenge, we propose a multi-task learning based blind calibraiton method for air quality sensors after deployments. Our method introduces not only the reference data of the target location to formulate calibration task, but also reference measurements collected from highly accurate stations already deployed by the government in other geographical locations to formulate prediction task. To utilize the reference measurements which are not in the same location with our target sensors, e.g., in other cities, we combine the proposed calibration task and prediction task under a multi-task learning scheme. The introduced references in other locations alleviate our few-reference challenge. Furthermore, we elaborate on the choices of different tasks to have better effect of the target calibraiton task. Evaluations on the real-world collected datasets show that our proposed algorithm has better calibraiton effect.

Fine-grained air pollution data is essential for smart living and efficient city management. However, it is arduous to obtain accurate air pollution data with high spatial and temporal resolutions via mobile crowdsensing (MCS) under limited budgets. Thus, we propose FAD, a system fully using fine-grained air pollution data to provide diverse services. Moreover, a low-cost yet highly accurate portable sensing device is designed for MCS applications to enhance data resolutions. Finally, we demonstrate various FAD-based services for citizens and governments in the real world.

Air pollution has become a global threat to human health. Fine-grained air quality monitoring has attracted much attention in recent years. Low-cost calibrated sensors make it possible for the large-scale deployment of IoT air quality monitoring systems. In practice, the calibration performance degrades after deployment due to the dynamic and diversity of system conditions. However, it is infeasible to collect sufficient in-field reference data to train calibration models for these new conditions. To address themulticonditionandfew-datachallenge, we proposed metalearning-based adaptive in-field calibration (MAIC), a metalearning-based adaptive in-field calibration algorithm. Specifically, MAIC adopts metalearning to learn how to adapt to new conditions quickly. To effectively leverage historical data, we first develop task generation strategies for sensor calibration under this scheme. Then, task-oriented optimization is introduced to train a model with superior adaptability in the offline training phase. Furthermore, an adaptation method is presented to learn the task-specific data distribution without forgetting the metaknowledge, enabling continual learning to utilize the temporal dependencies between multiple conditions. Our evaluations on synthetic and real-world data sets show that MAIC has high robustness and adaptability under multiple complicated conditions. Our proposed method outperforms the state-of-the-art calibration algorithms by 4.23%–29.46% in the real-world deployment data set, but with fewer requirements for the available in-field reference data.

Fine-grained city-scale outdoor air pollution maps provide important environmental information for both city managers and residents. Installing portable sensors on vehicles (e.g., taxis, Ubers) provides a low-cost, easy-maintenance, and high-coverage approach to collecting data for air pollution estimation. However, as non-dedicated platforms, vehicles like taxis usually prefer gathering at busy areas of a city where it is more likely to pick up riders. This leaves many parts of the city unsensed or less-sensed. In addition, due to the natural changes in a city and the movements of the vehicles, the sensed and unsensed areas change over time. Consequently, challenges of air pollution estimation with data collected by non-dedicated mobile platforms are twofold:i.data coverage is sparse;ii.data coverage changes over time. Therefore, the major research question is: how can we derive accurate and robust fine-grained field (e.g., air pollution) estimation given dynamic and sparse data collected from uncontrollable mobile sensing platforms? This paper presents adaptiveHMSS, an adaptivehybridmodel-enabledsensingsystem for fine-grained air pollution estimation with dynamic and sparse data collected from uncontrollable mobile sensing platforms, which is achieved by combining the advantages of aphysics guided modeland adata driven model. To address the challenge of sparse coverage, the physical understanding of the spatiotemporal correlation for air pollution distribution in thephysics guided modelis utilized to infer values at unsensed sparse areas. Meanwhile, thedata driven modelis adopted to estimate the air pollution influential factors (e.g., buildings) not included in thephysics guided model. To address the challenge of time-varying coverage, an adaptive model combination algorithm is designed to enable the system bias to either of the two models according to the amount of data collection and uncertainty of the model. To evaluate the system performance, we deployed 47 air pollution sensing devices on taxis and fixed locations in 2 cities for both controlled and uncontrolled experiments for over two weeks. The results show that with a resolution of$500 \;\mathrm m$by$500\;\mathrm m$by$1\;\mathrm {hour}$, our system achieves up to$3.2\times$error reduction when compared to the baseline approaches.

Air pollution problem has been a worldwide environmental concern in recent years. Accurate air pollution prediction can effectively protect public health and help government decisions. However, strong instability and frequent pattern shift in air pollution data challenges the conventional time series prediction paradigm and attracts interests in adaptive prediction algorithm. Recent progress in deep learning community, such as attention mechanism and meta learning algorithm, both use handcrafted adaptive strategy and lack sufficient usage of supporting observation data. In this paper, we adopt a variational Bayesian approach to enable fast adaption ability for a given air pollution predictor, which can make better use of recent observation data and adaptively inference task-specific parameters to achieve better adaption performance. Specifically, without explicitly designing a heuristic adaptive procedure, we formulate the adaptive prediction as a maximizing conditional likelihood problem on a generative graphic model, where a variational approximation to the intractable likelihood is further derived for end-to-end training. Experiments on real-world air pollution datasets show significant improvements of the proposed method compared to previous works.

As pollution problems become increasingly prominent nowadays, urban air quality monitoring has attracted more and more attention. In recent years, sensing systems based on low-cost sensors are proposed to achieve fine-grained monitoring with larger amount of deployment as supplyment to conventional monitoring stations. Calibration is critical to guarantee the accuracy and consistency of these sensing systems to fight against sensor drift. While conventional field calibration approaches often rely on real-time data from a nearby standard station, they are not applicable to low-cost sensors which cannot receive the latest reference data from nearby stations after deployment. In reality, it is very difficult for sensors to get access to nearby standard stations deployed sparsely. To reduce the dependency on real-time and nearby reference data, we present a Robust Calibration approach based on Historical data (RCH) for the low-cost air pollution sensor calibration. Our method corrects the sensor drift by adapting sensitivity and offset based on estimating the probability distribution of pollutant's concentration. Experiments with real-world NO2data in Foshan, China show that our proposed method acheives close performance to conventional field calibration methods but addresses above challenges. Moreover, our method can use historical data collected from the sensors in more distant geographic locations than the compared method.

Air pollution is a serious urban problem that threatens human health. Therefore, fine-grained pollution events detection has become a concerned issue for environmental management. Algorithms in previous studies identify pollution events as uptrend intervals at hour level. However, a significant part of pollution events caused by traffic and industry can be brief but frequent, which may be neglected under traditional coarse-grained detection. In this paper, we propose a fine-grained analysis of air pollution pattern based on minute-level event detection. Over the real-world deployment in Foshan, these events are analyzed according to their geographical contexts and temporal features. Results show insightful findings and this case study provides a practical reference for government inspection and pollution control.

In this paper, given the scenario of a mobile sensing system for air pollution monitoring, we aim at the cause and influence of delay effect on measurement and present a filter-based solution to calibrate the sensing data. We also validate the idea and solution by a real-data experiment. It indicates that the solution decreases deviation on spatial measurement and can be applied in mobile sensing systems to improve the sensing data quality.

Low-cost sensors are widely used to realize large-scale deployment for sensing systems. In this paper, we discuss challenges in using industrial-grade gas sensors for air quality monitoring. To overcome variation due to system errors, we present a framework for individualized calibration. Within the framework, multiple regression and interpolation methods are prepared for alternative optimization on fitting sensors' response to gas concentration.

This paper presents a hybrid adaptive particle filter (HAP) with online feedback to dynamically reconstruct high spatial-temporal resolution air pollution information from sparse vehicular based sensors. To deal with data sparsity, we apply both spatial and temporal correlation of air dispersion to reduce data dimension requirement. HAP adaptively predicts when the accumulated prediction error is low and then uses data compensation for correction whenever the prediction error becomes high. The preliminary results based on the city scale deployments with 10 taxis show that our system achieves up to 50% reduction on system errors.

According to the World Health Organization (WHO), outdoor air pollution led to an estimated 3.7 million premature deaths worldwide in 2012. To address this problem, it is necessary for both residents and city administrations to understand air quality in their immediate environment with fine-grained temporal-spatial resolution. Currently both fixed and mobile systems are used to attempt to sense the pollution field. However, they generally are expensive, cover small areas and thus result in lower accuracy.