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Fine particulate matter (PM2.5) and ozone (O3) air pollutants are known risk factors for asthma exacerbation. We studied the association of these air pollutants with pediatric asthma exacerbation in the Philadelphia metropolitan region, and evaluated potential effect modification by children's characteristics (e.g., race/ethnicity, atopic conditions) and environmental factors (e.g., neighborhood tree canopy, meteorological factors, aeroallergens). We conducted a time-stratified case-crossover study of 54,632 pediatric (age ≤18 years) asthma exacerbation cases occurring from 2011 to 2014, identified through electronic health records (EHR) of the Children's Hospital of Philadelphia (CHOP) health system. We applied conditional logistic regression to estimate associations between air pollution and asthma exacerbation, using daily census-tract level pollutant concentrations estimated from the EPA Fused Air Quality Surface Using Downscaling (FAQSD) files. The associations were estimated within warm (Apr-Sep) and cold (Oct-Mar) months for unlagged exposure and for cumulative effects up to 5 days after exposure, with adjustment for temperature, relative humidity, and holidays. We found small increases in odds of asthma exacerbation with higher pollutant concentrations, with positive associations (OR, comparing concentrations of 75th to 25th percentile) observed for PM2.5 during both warm (1.03, 95% CI: 0.98-1.08) and cold months (1.05, 95% CI: 1.02-1.07), and for O3 during cold months (1.08, 95% CI: 1.02-1.14). The exposure-response relationship with PM2.5 during the cold months was essentially linear, whereas thresholds of effect were observed for the other associations at low-medium pollutant concentrations. Results were robust to multi-pollutant modeling and adjustment for additional covariates. We found no effect modification by most children's characteristics, while effect sizes were higher on days with detected tree and grass pollens during warm months. Our results suggest that even small decreases in pollutant concentrations could potentially reduce risk of childhood asthma exacerbation - an important finding, given the high burden of childhood asthma and known disparities in asthma control.

The electricity market in the U.S. is changing rapidly from a utility-scale centralized generation-distribution model to a more distributed and customer-sited energy model. Increasingly, residential consumers are showing interest in solar-based electricity, which has resulted in increased adoption of distributed solar on the rooftops of owner-occupied residences. However, limited accessibility of rooftop photovoltaic (PV) has led to equity concerns among policymakers. Also, utility companies face a decline in revenues as more residents adopt rooftop PV. In response to these issues, utility companies must consider providing alternative renewable energy options to their customers and incorporate consumer adoption modeling in their expansion planning approach. Agent-based modeling enables energy consumers' socially-motivated adoption decisions to be realistically captured. This paper describes an agent-based model that demonstrates the value of incorporating consumer-adoption modeling in a utility company's expansion planning approach.

This article studies full-waveform associated identification method of airborne time-domain electromagnetic method (ATEM) 3-d anomalies based on multiple linear regression analysis method. By using convolution algorithm, full-waveform theoretical responses are computed to derive sample library including switch-off-time period responses and off-time period responses. Extract full-waveform attributes from theoretical responses to derive linear regression equations which are used to identify the geological parameters. In order to improve the precision ulteriorly, we optimize the identification method by separating the sample library into different groups and identify the parameter respectively. Performance of full-waveform associated identification method with field data of wire-loop test experiments with ATEM system in Daedao of Changchun proves that the full-waveform associated identification method is feasible practically.

As an important supplement and development of traditional methods, the meshfree method has received a great deal of attention in the field of engineering calculation, and has been successfully used to solve many problems which traditional methods have difficulty in solving. However, the application of meshfree method is relatively less in the area of geophysics. In this paper, we apply the meshfree method to the numerical simulation of geophysical electromagnetic prospecting, taking the 2D magnetotelluric as an example and deduce the corresponding meshfree radial point interpolation method (RPIM) equivalent linear equations in detail. The high-efficiency and accurate solutions of large-scale sparse linear equations are solved by the quasi-minimal residual method based on Krylov subspace. The optimal values of the shape parameters are given by numerical experiments. The correctness of the meshfree method is verified by a layered model. The root mean square error of the calculation results is no more than 0.35%, its accuracy is superior to the finite element method. We also compare the meshfree solution with FEM solution by calculating an inclined vein body model, and the calculation results are in good agreement. A continuously changing fault model and undulating terrain model which traditional methods have difficulty in simulating are respectively calculated, the sectional profiles of the apparent resistivity accurately reflect the trend of the anomalies. The meshfree method does not require the complicated mesh generation, and the physical parameters are loaded at a series of points, thus it is especially suitable for the calculation of the complex geological models. With the rapid development of computational science, the meshfree techniques will certainly become a new robust numerical simulation method in geophysical electromagnetic prospecting.