Impacts of different urban canopy schemes in WRF/Chem on regional climate and air quality in Yangtze River Delta, China

AbstractYangtze River Delta (YRD) region has experienced a remarkable urbanization during the past 30years, and regional climate change and air pollution are becoming more and more evident due to urbanization. Impacts of urban canopy on regional climate and air quality in dry- and wet-season are investigated in this paper, utilizing the Weather Research and Forecasting/Chemistry (WRF/Chem) model. Four regimes of urban canopy schemes with updated USGS land-use data in actual state of 2004 base on MODIS observations are examined: (1) SLAB scheme that does not consider urban canopy parameters (the control experiment in this paper); (2) a single-layer urban model with a fixed diurnal profile for anthropogenic heat (UCM); (3) multilayer urban canopy model (BEP-Building effect parameterization); (4) multilayer urban models with a building energy model including anthropogenic heat due to air conditioning (BEP+BEM). Results show that, compared with observations, the best 2-m temperature estimates with minimum bias are obtained with SLAB and BEP+BEM schemes, while the best 10-m wind speed predictions are obtained with BEP and BEP+BEM scheme. For PM10 and ozone predictions, BEP+BEM scheme predicted PM10 well during January, while the best estimate of PM10 is obtained with UCM scheme during July, BEP+BEM and SLAB schemes best estimated ozone concentrations for both the two months. Spatial differences of meteorological factors between canopy schemes and control scheme show that compared with SLAB scheme, BEP and BEP+BEM schemes cause an increase of temperature with differences of 0.5°C and 0.3°C, respectively, UCM scheme simulates lower temperature with decrease of 0.7°C during January. In July, all the canopy experiments calculates lower air temperature with reduction of 0.5°C–1.6°C. All the canopy experiments compute lower 10-m wind speed for both January and July. Decreases were 0.7m/s (0.8m/s) with UCM, 1.7m/s (2.6m/s) with BEP, and 1.8m/s (2.3m/s) with BEP+BEM schemes in January (July), respectively. For chemical field distributions, results show that, compared with SLAB scheme, UCM scheme calculates higher PM10 concentration in both January and July, with the differences of 22.3% (or 24.4μg/m3) in January, and 31.4% (or 17.4μg/m3) in July, respectively. As large as 32.7% (or 18.3 μg/m3) of PM10 increase is found over Hangzhou city during July. While 18.6% (or 22.1 μg/m3) and 16.7% (or 24.6 μg/m3) of PM10 decreases are fund in BEP and BEP+BEM schemes during January. Compared with control experiment during January, 6.5% (or 2.6ppb) to 10.4% (4.2ppb) increases of ozone are computed over mage-cities by canopy experiments. All the three canopy schemes predict lower ozone concentrations and as large as 30.2% (or 11.2ppb) decrease is obtained with UCM scheme, and 16.5% (6.2ppb) decrease with BEP scheme during July. The SLAB scheme is suitable for real-time weather forecast while multiple urban canopy scheme is necessary when quantify the urbanization impacts on regional climate

Study of PBLH and Its Correlation with Particulate Matter from One-Year Observation over Nanjing, Southeast China

The Planetary Boundary Layer Height (PBLH) plays an important role in the formation and development of air pollution events. Particulate Matter is one of major pollutants in China. Here, we present the characteristics of PBLH through three-methods of Lidar data inversion and show the correlation between the PBLH and the PM2.5 (PM2.5 with the diameter 75 μg/m3 and the PM2.5 \u3c 35 μg/m3 in daytime, respectively. The low PBLH often occurs with condition of the low wind speed and high relative humidity, which will lead to high PM2.5 concentration and the low visibility. On the other hand, the stability of PBL is enhanced by high PM concentration and low visibility

A Satellite-Driven Model to Estimate Long-Term Particulate Sulfate Levels and attributable Mortality Burden in China

Ambient fine particulate matter (P

Preparation of Hierarchical Porous Silicalite-1 Encapsulated Ag NPs and Its Catalytic Performance for 4-Nitrophenol Reduction

Abstract A facile and efficient strategy is presented for the encapsulation of Ag NPs within hierarchical porous silicalite-1. The physicochemical properties of the resultant catalyst are characterized by TEM, XRD, FTIR, and N2 adsorption-desorption analytical techniques. It turns out that the Ag NPs are well distributed in MFI zeolite framework, which possesses hierarchical porous characteristics (1.75, 3.96 nm), and the specific surface area is as high as 243 m2 · g−1. More importantly, such catalyst can rapidly transform the 4-nitrophenol to 4-aminophenol in aqueous solution at room temperature, and a quantitative conversion is also obtained after being reused 10 times. The reasons can be attributed to the fast mass transfer, large surface area, and spatial confinement effect of the advanced support

Improved Intelligent Image Segmentation Algorithm for Mechanical Sensors in Industrial IoT: A Joint Learning Approach

The industrial Internet of Things (IoT) can monitor production in real-time by collecting the status of parts on the production line with cameras. It is easy to have bright and dark areas in the same image because of the smooth surfaces of mechanical parts and the unstable light source, which affects semantic segmentation’s performance. This paper proposes a joint learning method to eliminate the influence of illumination on semantic segmentation. Semantic image segmentation and image decomposition are jointly trained in the same model, and the reflectance image is used to guide the semantic segmentation task without the illumination component. Moreover, this paper adopts an enhanced convolution kernel to improve the pixel accuracy and BN fusion to enhance the inference speed, optimizing the model to meet real-time detection needs. In the experiments, a dataset of real gear parts was collected from industrial IoT cameras. The experimental results show that the proposed joint learning approach outperforms the state-of-the-art methods in the task of edge mechanical part detection, with about 4% pixel accuracy improvement