Rui Ma

Bio: Rui Ma is an academic researcher from Tsinghua University. The author has contributed to research in topics: Air quality index & Autoencoder. The author has an hindex of 5, co-authored 13 publications receiving 92 citations.

Reducing Indoor Levels of "Outdoor PM2.5" in Urban China: Impact on Mortalities.

Abstract: This study estimates adult mortalities attributed to PM2.5 across urban China in 2015 and the corresponding mortalities that might be avoided by meeting the yearly averaged indoor PM2.5 threshold i...

Guiding the Data Learning Process with Physical Model in Air Pollution Inference

Abstract: The surveillance of air pollution is becoming a highly concerned issue for city residents and urban administrators. Fixed air quality stations as well as mobile gas sensors have been deployed for air quality monitoring but with sparse observations over the entire temporal-spatial space. Therefore, an inference algorithm is essential for comprehensive fine-grained air pollution sensing. Conventional physically-based models can hardly be applied to all the scenarios, while pure data-driven methods suffer from sampling bias and overfitting problems. This paper presents a hybrid algorithm for air pollution inference by guiding the data learning process with physical model. The quantitative combination of knowledge from observed dataset and a discretized convective-diffusion model is performed within a multi-task learning scheme. Evaluations show that, benefited from physical guidance, our hybrid method obtains higher extrapolation ability and more robustness, achieving the same performance with 1/8 sample amount and obtaining 31.9% less error in noisy synthesized environment. In a real-world deployment in Tianjin, our algorithm outperforms the pure data-driven model with 9.69% less inference error over a 9-day PM 2.5 data collection.

Fine-Grained Air Pollution Inference with Mobile Sensing Systems: A Weather-Related Deep Autoencoder Model

Abstract: Air pollution is a global health threat. Except static official air quality stations, mobile sensing systems are deployed for urban air pollution monitoring to achieve larger sensing coverage and greater sampling granularity. However, the data sparsity and irregularity also bring great challenges for pollution map recovery. To address these problems, we propose a deep autoencoder framework based inference algorithm. Under the framework, a partially observed pollution map formed by the irregular samples are input into the model, then an encoder and a decoder work together to recover the entire pollution map. Inside the decoder, we adopt a convolutional long short-term memory (ConvLSTM) model by revealing its physical interpretation with an atmospheric dispersion model, and further present a weather-related ConvLSTM to enable quasi real-time applications. To evaluate our algorithm, a half-year data collection was deployed with a real-world system on a coastal area including the Sino-Singapore Tianjin Eco-city in north China. With the resolution of 500 m x 500 m x 1 h, our offline method is proved to have high robustness against low sampling coverage and accidental sensor errors, obtaining 14.9% performance improvement over existing methods. Our quasi real-time model better captures the spatiotemporal dependencies in the pollution map with unevenly distributed samples than other real-time approaches, obtaining 4.2% error reduction.

Inferring fine-grained air pollution map via a spatiotemporal super-resolution scheme

Abstract: Air pollution monitoring is a concerned issue in urban management. Nowadays, vehicle-based mobile sensing systems are deployed to enhance sensing granularity. However, in these systems, the number of online sensors may change over time and the long-term maintenance is costly. Therefore, an inference algorithm is necessary to maintain the high spatiotemporal granularity of pollution field under both dense and sparse sampling. In this paper, we propose an algorithm based on super-resolution scheme. To address the challenges of complex external factors and spatiotemporal dependencies, three modules are included: a heterogeneous data fusion subnet to extract useful information from external data, a spatiotemporal residual subnet to capture the spatiotemporal dependencies in pollution field, and an upsampling subnet to generate the fine-grained pollution map. Experiments on real-world datasets show that our model outperforms the state-of-the-art baselines.

Residential solid fuel emissions contribute significantly to air pollution and associated health impacts in China.

Abstract: Residential contribution to air pollution-associated health impacts is critical, but inadequately addressed because of data gaps. Here, we fully model the effects of residential energy use on emissions, outdoor and indoor PM2.5 concentrations, exposure, and premature deaths using updated energy data. We show that the residential sector contributed only 7.5% of total energy consumption but contributed 27% of primary PM2.5 emissions; 23 and 71% of the outdoor and indoor PM2.5 concentrations, respectively; 68% of PM2.5 exposure; and 67% of PM2.5-induced premature deaths in 2014 in China, with a progressive order of magnitude increase from sources to receptors. Biomass fuels and coal provided similar contributions to health impacts. These findings are particularly true for rural populations, which contribute more to emissions and face higher premature death risks than urban populations. The impacts of both residential and nonresidential emissions are interconnected, and efforts are necessary to simultaneously mitigate both emission types.

The rise of low-cost sensing for managing air pollution in cities

Abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

The atmospheric chemistry of indoor environments

Abstract: Through air inhalation, dust ingestion and dermal exposure, the indoor environment plays an important role in controlling human chemical exposure. Indoor emissions and chemistry can also have direct impacts on the quality of outdoor air. And so, it is important to have a strong fundamental knowledge of the chemical processes that occur in indoor environments. This review article summarizes our understanding of the indoor chemistry field. Using a molecular perspective, it addresses primarily the new advances that have occurred in the past decade or so and upon developments in our understanding of multiphase partitioning and reactions. A primary goal of the article is to contrast indoor chemistry to that which occurs outdoors, which we know to be a strongly gas-phase, oxidant-driven system in which substantial oxidative aging of gases and aerosol particles occurs. By contrast, indoor environments are dark, gas-phase oxidant concentrations are relatively low, and due to air exchange, only short times are available for reactive processing of gaseous and particle constituents. However, important gas–surface partitioning and reactive multiphase chemistry occur in the large surface reservoirs that prevail in all indoor environments. These interactions not only play a crucial role in controlling the composition of indoor surfaces but also the surrounding gases and aerosol particles, thus affecting human chemical exposure. There are rich research opportunities available if the advanced measurement and modeling tools of the outdoor atmospheric chemistry community continue to be brought indoors.