Geographic routing has been considered as an attractive approach for wireless sensor networks (WSNs) since it exploits local location information instead of global topology information to route data. However, it often suffers from the routing hole in various environments during data delivery, resulting in route failure. Currently, existing geographic routing tends to walk along only one side of the routing holes to recover the route, thus achieving suboptimal network performance such as longer delivery delay and lower delivery ratio. Furthermore, they cannot guarantee that all packets are delivered in an energy-efficient manner once encountering routing holes. In this paper, we propose an energy-aware dual-path geographic routing (EDGR) to address these issues. EDGR utilizes the location information, residual energy, and the characteristics of energy consumption to make routing decisions, and dynamically exploits two node-disjoint anchor lists, passing through two sides of the routing holes, to shift routing path for load balance. Moreover, we extend EDGR into 3D WSNs to provide energy-aware routing for routing hole detour. Simulation results demonstrate that EDGR is superior to existing geographic routing in terms of energy efficiency, network lifetime, packet deliver ratio and delivery delay in WSNs over a variety of communication sessions passing through routing holes.

Climate actions targeting combustion sources can generate large ancillary health benefits via associated air-quality improvements. Therefore, understanding the health costs associated with ambient fine particulate matter (PM_2·5) from combustion sources can guide policy design for both air pollution and climate mitigation efforts. In this modelling study, we estimated the health costs attributable to ambient PM_2·5 from six major combustion sources across 204 countries using updated concentration-response models and an age-adjusted valuation method. We defined major combustion sources as the sum of total coal, liquid fuel and natural gas, solid biofuel, agricultural waste burning, other fires, and 50% of the anthropogenic fugitive, combustion, and industrial dust source. Global long-term exposure to ambient PM_2·5 from combustion sources imposed US$1·1 (95% uncertainty interval 0·8-1·5) trillion in health costs in 2019, accounting for 56% of the total health costs from all PM_2·5 sources. Comparing source contributions to PM_2·5 concentrations and health costs, we observed a higher share of health costs from combustion sources compared to their contribution to population-weighted PM_2·5 concentration across 134 countries, accounting for more than 87% of the global population. This disparity was primarily attributed to the non-linear relationship between PM_2·5 concentration and its associated health costs. Globally, phasing out fossil fuels can generate 23% higher relative health benefits compared to their share of PM_2·5 reductions. Specifically, the share of health costs for total coal was 36% higher than the source's contributions to corresponding PM_2·5 concentrations and the share of health costs for liquid fuel and natural gas was 12% higher. Other than fossil fuels, South Asia was expected to show 16% greater relative health benefits than the percentage reduction in PM_2·5 from the abatement of solid biofuel emissions. In most countries, targeting combustion sources might offer greater health benefits than non-combustion sources. This finding provides additional rationale for climate actions aimed at phasing out combustion sources, especially those related to fossil fuels and solid biofuel. Mitigation efforts designed according to source-specific health costs can more effectively avoid health costs than strategies that depend solely on the source contributions to overall PM_2·5 concentration. The Health Effects Institute, the National Natural Science Foundation of China, and NASA.

Background: Particulate air pollution, especially PM_2.5, is highly correlated with various adverse health impacts and, ultimately, economic losses for society, however, few studies have undertaken a spatiotemporal assessment of PM_2.5-related economic losses from health impacts covering all of the main cities in China. Methods: PM_2.5 concentration data were retrieved for 190 Chinese cities for the period 2014–2016. We used a log-linear exposure–response model and monetary valuation methods, such as value of a statistical life (VSL), amended human capital (AHC), and cost of illness to evaluate PM_2.5-related economic losses from health impacts at the city level. In addition, Monte Carlo simulation was used to analyze uncertainty. Results: The average economic loss was 0.3% (AHC) to 1% (VSL) of the total gross domestic product (GDP) of 190 Chinese cities from 2014 to 2016. Overall, China experienced a downward trend in total economic losses over the three-year period, but the Beijing–Tianjin–Hebei, Shandong Peninsula, Yangtze River Delta, and Chengdu-Chongqing regions experienced greater annual economic losses. Conclusions: Exploration of spatiotemporal variations in PM_2.5-related economic losses from long-term health impacts could provide new information for policymakers regarding priority areas for PM_2.5 pollution prevention and control in China.