A New Collaborative Multi-Agent Monte Carlo Simulation Model for Spatial Correlation of Air Pollution Global Risk Assessment

Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index-based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time-series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series-based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short-term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi-agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real-world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models.

Low-likelihood high-warming storylines for extremes

<p>Recent IPCC focused their assessment of changes in climate extremes primarily on the <em>likely</em> range and on mapping them as multi-model means. Recently, it has been argued that focusing primarily on the <em>likely</em> range potentially ignores changes in the physical climate system that are unlikely to occur but are associated with the highest risks for human and ecological systems. This is particularly the case for extremes where impacts often non-linearly depend on changes in hazards and where uncertainties are typically large both due to model response uncertainty and internal variability. Low-likelihood high-warming storylines have been proposed as a powerful tool to assess and communicate the risk associated with such future climates. However, storylines that are consistent across variables and spatial patterns is challenging.</p><p>Here, we introduce and compare different approaches for creating low-likelihood high-warming storylines for extremes based on CMIP6 models, and discuss their strengths and limitations for temperature extremes, heavy rainfall and droughts. We demonstrate that all approaches yield storylines in which changes in hot extremes, extreme rainfall and droughts strongly exceed the multi-model mean over large parts of the globe. This suggests that a focus on the likely range may indeed substantially underestimate the risk associated with changes in extremes.</p><p>We further demonstrate that the choice of the storyline approach needs to be informed by the purpose of the assessment. Pattern-scaling based storyline approaches are simple and easy to communicate and provide a reasonable first guess for extremes that are closely related to temperature changes. However, they often lead to implausible global patterns and violate physical consistency across regions and different variables. Particularly for wet and dry extremes, the models showing the largest global warming often do not show the greatest changes in extremes. Other more complex approaches have the advantage of generating storylines of globally coherent patterns of changes in extremes. Such approaches allow assessing physically consistent and spatially coherent global low-likelihood high-warming storylines of regional extremes that are suited for global risk assessment and resilience building across different sectors.</p>

How India Fights with COVID-19 with Learning from Highly Affected Countries

: It was 11 March 2020 when the World Health Organization (WHO) declared the name COVID-19 for coronavirus disease and also described it as a pandemic. Till that day 118,000 cases were confirmed of pneumonia with breathing problem throughout the world. At the start of New Year when COVID-19 came into knowledge a few days later, the gene sequencing of the virus was revealed. Today the number of confirmed cases is scary, i.e. 9,472,473 in the whole world and 484,236 deaths have been recorded by WHO till 26 June 2020. WHO's global risk assessment is very high [1]. The report is enlightening the lessons learned by India from the highly affected countries.