Air pollution is a persistent and well-established public health problem in India: emissions from coal-fired power plants have been associated with over 80,000 premature deaths in 2015. Premature deaths could rise by four to five times this number by 2050 without additional pollution controls. We site a model 500 MW coal-fired electricity generating unit at eight locations in India and examine the benefits and costs of retrofitting the plant with a flue-gas desulfurization unit to reduce sulfur dioxide emissions. We quantify the mortality benefits associated with the reduction in sulfates (fine particles) and value these benefits using estimates of the value per statistical life transferred to India from high income countries. The net benefits of scrubbing vary widely by location, reflecting differences in the size of the exposed population. They are highest at locations in the densely populated north of India, which are also among the poorest states in the country.

Infrared thermographs (IRTs, also called thermal cameras) have been used to remotely measure elevated body temperature (BT) and respiratory rate (RR) during infectious disease outbreaks, such as COVID-19. To facilitate the fast measurement of BT and RR using IRTs in densely populated venues, it is desirable to have IRT algorithms that can automatically identify the best facial locations in thermal images to extract these vital signs. The IEC 80601-2-59:2017 standard suggests that the regions medially adjacent to the inner canthi of the eyes are robust BT measurement sites. The nostril regions, on the other hand, are often used for RR estimation. However, it is more difficult to automatically identify inner canthi and nostrils in thermal images than in visible-light images, which are rich with exploitable features. In this paper, a unique system that can detect inner canthi and outer nostril edges directly in thermal images in two phases is introduced. In Phase I, original thermal images were processed in four different ways to enhance facial features to facilitate inner canthus and nostril detection. In Phase II, landmarks of the inner canthi and outer nostril edges were detected in two steps: (1) face detection using the Single Shot Multibox Detector (SSD) and (2) facial landmark detection to locate the inner canthi and outer nostril edges. The face detection, facial landmark detection, and overall system accuracies were evaluated using the intersection over union, normalized Euclidean distance, and success detection rate metrics on a set of 36 thermal images collected from 12 subjects using three different IRTs. Additional validation was performed on a subset of 40 random thermal images from the publicly available Tufts Face Database. The results revealed that the processed images-referred to as ICLIP images-yielded the highest landmark localization accuracy from the four types of processed thermal images, verifying that the system can automatically and accurately estimate the inner canthus and nostril locations in thermal images. The proposed system can be applied in IRT algorithms to provide reliable temperature measurements and RR estimates during infectious disease outbreaks.

<p>Recent studies have shown that subtle changes in human face color due to heartbeats can be captured by regular RGB digital video cameras. It is possible, though challenging, to track one's pulse rate when a video contains significant subject's body motions in a fitness setting. The robustness gain in the recently proposed systems is often achieved by adding or changing certain modules in the system's pipeline. Most existing works, however, only evaluate the performance of the pulse rate estimation at the system level of particular pipeline configurations, whereas the contribution from each module remains unclear. To gain a better understanding of the performance at the module level and facilitate future research in explainable learning and artificial intelligence (AI) in physiological monitoring, this paper conducts an in-depth comparative study at the module level for video-based pulse rate tracking algorithms; a special focus is placed on challenging fitness scenarios involving significant movement. The representative efforts over the past decade in the field are reviewed, upon which a reconfigurable rPPG framework/pipeline is constructed comprising of major processing modules. For performance attribution, different candidates for each module are evaluated while having the rest of modules fixed. The performance evaluation is based on a signal quality metric and four pulse-rate estimation metrics and uses the simultaneously recorded ECG-based heart rate measurement as a reference. Experimental results using a challenging fitness dataset reveals the synergy between pulse color mapping and adaptive motion filtering in obtaining accurate pulse rate estimates. The results also suggest the importance of robust frequency tracking for accurate PR estimation in low signal-to-noise ratio fitness scenarios.</p>