Spatial distribution of average values of parameters (a) CentralHeat (b) AOD*diff (c) R_AOD*diff from 2004–2012.

<p>The blue line along the Qin Mountains and Huai River is the traditional dividing line between north and south China.</p

Spatial distribution of average AOD*annual from 2004–2012.

<p>The blue line along the Qin Mountains and Huai River is the traditional dividing line between north and south China. AOD^*_annual varied greatly across the study domain and north China has higher aerosol loading relative to south China generally.</p

Summary statistics of meteorological and social-economic parameters.

<p>Summary statistics of meteorological and social-economic parameters.</p

Average AOD^* in different spatio-temporal groups.

<p>The black line shows the AOD^*_annual over the entire study region as a reference. The average AOD^* during the heating season in the heating area was consistently higher than other spatio-temporal groups.</p

Estimates of parameters in the linear regression models.

<p>^ap-value< 0.01</p><p>Estimates of parameters in the linear regression models.</p

Estimating Ground-Level PM_2.5 in China Using Satellite Remote Sensing

Estimating ground-level PM_2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM_2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM_2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM_2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) <i>R</i>² is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m³. The mean prediction error (MPE) of the predicted annual PM_2.5 is 8.28 μg/m³. Our predicted annual PM_2.5 concentrations indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Standard (CNAAQS) Level 2 standard. Our results also confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM_2.5 monitoring network in China

Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between −3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management

Integrating Augmented <i>In Situ</i> Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019

Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-based aerosol optical depth before 2000, has resulted in a gap in spatiotemporally resolved PM2.5 data for historical periods. Taking the United Kingdom as an example, we leveraged the light gradient boosting model to capture the spatiotemporal association between PM2.5 concentrations and multi-source geospatial predictors. Augmented PM2.5 from PM10 measurements expanded the spatiotemporal representativeness of the ground measurements. Observations before and after 2009 were used to train and test the models, respectively. Our model showed fair prediction accuracy from 2010 to 2019 [the ranges of coefficients of determination (R2) for the grid-based cross-validation are 0.71–0.85] and commendable back extrapolation performance from 1998 to 2009 (the ranges of R2 for the independent external testing are 0.32–0.65) at the daily level. The pollution episodes in the 1980s and pollution levels in the 1990s were also reproduced by our model. The 4-decade PM2.5 estimates demonstrated that most regions in England witnessed significant downward trends in PM2.5 pollution. The methods developed in this study are generalizable to other data-rich regions for historical air pollution exposure assessment

Probing Exciton Move and Localization in Solution-Grown Colloidal CdSe_<i>x</i>S_1–<i>x</i> Alloyed Nanowires by Temperature- and Time-Resolved Spectroscopy

Colloidal semiconductor nanowires are interesting materials with polarized optical feature for optoelectronics devices. Previously, we observed an interesting photoluminescence enhancement in colloidal alloyed CdSe_<i>x</i>S_1–<i>x</i> nanowires. In the present work, low temperature steady-state and time-resolved photoluminescence spectra were applied to understand the photoluminescence enhancement in these CdSe_<i>x</i>S_1–<i>x</i> alloyed nanowires. The band-edge emission and surface-defect emission of alloyed CdSe_<i>x</i>S_1–<i>x</i> nanowires, observed in low temperature photoluminescence spectra, show different changing trend with the variation of their composition. Moreover, the radiative lifetime for band-edge emission and surface-defect emission reveals an opposite changing trend with the variation of temperature. These findings suggest that the variation of photoluminescence quantum yields with composition is determined by the competition between exciton move and localization. If the carriers are localized in the interior of nanowires, the migration of photoinduced excitons to their surface will be prohibited, and more probability for radiative recombination at band edge occurred

Acceleration of Liquid–Solid Redox Reaction with a Magneto-Catalyzed Method

To accelerate the chemical reaction is a key issue in the studies of catalytic chemistry. Here, by taking liquid–solid redox reaction Zn/CuSO4 as a model system, we present a remote and nontouched magneto-catalyzed method that can accelerate the chemical reaction efficiently. The effects from intensity (B) and intensity × gradient (B∇B) of applied magnetic field are distinguished, and the dominant role played by the B has been confirmed. With B increasing, the more of Zn–Cu galvanic cells and the bigger area of Cu/Cu2+ interfacial could be realized via a magnetohydrodynamics effect, which were proved by both optical and electron microscopic observations. It was found that 22 times enhancement of reaction rate and 7700 J/mol reduction of activation energy were achieved when an 8.4 T magnetic field was applied. These observations provide a magneto-catalyzed method to modulate the chemical reaction