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Modeling Clear Sky Solar Radiation
Published in Daryl R. Myers, Solar Radiation, 2017
The results of the regression analysis for elevations from sea level to 7000 m, water vapor from 0.2 to 10 atm-cm, and AOD (at 700 nm, or equivalent to BBAOD) from 0 to 0.45 were [17] Io′= 1618tg =0.464g =0.402tb =0.606b =0.491td = 2.698d =0.187
2.5 Concentrations in a Metropolitan Region
Published in Ni-Bin Chang, Kaixu Bai, Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, 2018
Overall, even with the aid of data merging, it is still incapable of creating a spatially complete AOD image over the study area. From Figures 19.8 through 19.11, it is clear that the AOD map for the winter time had many more data gaps (value missing pixels) than those in other seasons. Restoring these data gaps can be made possible with the aid of interpolation/extrapolation methods or memory effects. The SMIR method via time-space-spectrum continuum is worthwhile to apply for bridging such a gap to recover the missing pixels via memory effects (see Chapter 12) (Chang et al., 2015). Results of the reconstructed AOD by making use of SMIR will be described in section 19.5.4.
Securing the long-term sustainability of black-tailed godwits at the Nene Washes, UK, against climate change and its impact on hydrological conditions
Published in Wim Uijttewaal, Mário J. Franca, Daniel Valero, Victor Chavarrias, Clàudia Ylla Arbós, Ralph Schielen, Alessandra Crosato, River Flow 2020, 2020
N.J. Clemenz, D. Ocio, C. Hudson, H. Ward, C. Kitchin
Elevation of the Nene Washes ranges from sea-level to 7.6m AOD (based on the Environment Agency (2019) LiDAR composite dataset). The mean elevation is 1.5m AOD. Average annual precipitation across the Washes is 616mm (Standardised Annual Average Rainfall 1961-1990) (Centre for Ecology and Hydrology 2019).
A novel combined model based on echo state network – a case study of PM10 and PM2.5 prediction in China
Published in Environmental Technology, 2020
Hairui Zhang, Zhihao Shang, Yanru Song, Zhaoshuang He, Lian Li
However, because these methods are based on linear analysis method, they cannot handle the nonlinear fitting problem well. In recent years, researchers have begun to use machine learning prediction methods to deal with nonlinear data, a large number of literatures show that machine learning methods can get good prediction results. Among these methods, ANN has been widely applied to forecast the PM pollutants (especially PM10 and PM2.5) [7]. We can draw a conclusion from the study of Fernando et al. [8] that ANN is much easier, quicker to implement without compromising the accuracy of predictions. De et al. [9] developed a new ANN to forecast daily PM10 concentration. Antanasijević et al. [10] developed an ANN model for the forecasting of annual PM10 emission, the results are more than three times better than the results obtained from the conventional multi-linear regression and principal component regression models. Taking into account the spatial nonstationary relationship between PM 2.5 and AOD, Zou et al. [11] employed geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM 2.5 concentrations. Using meteorological and chemical variables as inputs, Biancofiore et al. adopted a more sophisticated recurrent neural architecture to predict PM10 and PM2.5 concentrations from one to three days ahead. Apart from this, Park et al. [12] predict indoor PM10 concentration on selected stations by ANN model.
Broadband dependence of atmospheric transmissions in the UV and total solar radiation
Published in Tellus B: Chemical and Physical Meteorology, 2019
Hana Lee, Woogyung Kim, Yun G. Lee, Ja-Ho Koo, Yeonjin Jung, Sang S. Park, Hi-Ku Cho, Jhoon Kim
To calculate the individual cloud, aerosol, and ozone transmissions, the SSR (EUV, TUV, and GS) values were corrected for one factor while the other two were kept constant. Thus cloud transmission was calculated under constant AOD and TCO atmospheric conditions, using Equation (8), and a similar procedure applied for aerosol transmission under constant CC and TCO atmospheric conditions, and for ozone transmission under constant CC and AOD atmospheric conditions. Then, from the corrected SSR values, the individual transmission of clouds, aerosols, and ozone was also calculated, and the results obtained are shown in Fig. 3b–d, respectively. The annual patterns of monthly average cloud transmission for the EUV, TUV, and GS regions (Fig. 3b) reached their minimum of 69.9%, 58.0%, and 50.8% in July with an annual average of 78.4%, 73.9%, and 71.7%, respectively, with the maximum CC during the rainy season. The corresponding maximum cloud transmissions of 86.8%, 84.5%, and 82.6% were in January in all three spectral regions. As shown in Fig. 3c, the aerosol transmission ranged from 70.1% (June) to 87.2% (January) with an annual average of 79.4% in the EUV, from 59.1% (July) to 84.9% (January) with an annual average of 75.1% in the TUV, and from 52.7% (July) to 85.4% (February) with an annual average of 74.1% in the GS. This represents increased scattering at shorter UV wavelengths than at longer wavelengths. Cho and Kang (1984) investigated aerosol characteristics using the WMO/National Oceanic and Atmospheric Administration/Environmental Protection Agency (NOAA/EPA) atmospheric turbidity data (1972–8) measured at 57 WMO air-pollution network stations, including Seoul. Their results showed that over this period the monthly average and minimum AOD at 500 nm at sea level in the Northern Hemisphere are 0.18 and 0.01, respectively, corresponding to AOD transmissions of 66.0% and 80.0%. The annual values of the monthly average and minimum AOD in Seoul are 0.19 and 0.103, corresponding to aerosol transmissions of 65% and 79%, respectively. Therefore, the transmissions in Seoul are quite close to the northern Northern Hemispheric sea-level averages, even though the Seoul values are higher than the equivalent values at over the 57 stations. As shown in Fig. 3d, the ozone transmission ranged from 69.8% (July) to 87.6% (January) with an annual average of 78.9% in the EUV, from 58.8% (July) to 85.5% (January) with an annual average of 74.9% in the TUV, and a range of 52.5% (July) to 85.4% (February) with an annual average of 74.1% in the GS region. Consequently, the annual averages of atmospheric transmission have similar values in the different spectral regions. It is comparable results as expected from Table 3 with the range of the correction ratio between 1.02 and 0.99.
Health risk assessment using chemical signatures of fine and coarse particles collected at breathing level height during firework display in New Delhi, India
Published in Human and Ecological Risk Assessment: An International Journal, 2022
Fine (PM2.5) and coarse (PM2.5-10) particle average mass concentrations with standard deviation before Diwali (BD), during Diwali (DD) and after Diwali (AD) in 2017 (fireworks ban year) and 2018 (no ban year) are given in Table 1 and plotted in Figure 2. PM10 levels (not measured directly rather represented as sum of PM2.5 and PM2.5-10) were highest during BD (608.5 µg m−3) followed by AD (336.2 µg m−3) and DD (257.7 µg m−3) at BLH in fireworks ban year (2017). Contrary to this, calculated PM10 was highest during DD (1800 µg m−3) followed by AD (424.5 µg m−3) and BD (371.2 µg m−3) in fireworks no ban year (2018). The observed values are quite similar with previous study during fireworks display reported at Bhilai (Pervez et al. 2015), Dehradun (Prabhu et al. 2019) and New Delhi (Kumar et al. 2018) in India. PM2.5 and PM2.5-10 levels were highest during BD (458.9 µg m−3 and 149.6 µg m−3) followed by AD (224.4 µg m−3 and 111.8 µg m−3) and DD (192.9 µg m−3 and 64.8 µg m−3) in 2017 while highest values were observed during DD (1565.3 µg m−3 and 235.0 µg m−3) followed by AD (317.9 µg m−3 and 106.6 µg m−3) and BD (285.9 µg m−3 and 85.3 µg m−3) in 2018. PM2.5 levels during Diwali in 2018 were nearly 26 times high compared to National Ambient Air Quality Standard (NAAQS: 60 µg m−3 for 24 hours). This could be direct evidence of impact of fireworks display on particle load in the year 2018 compared to that in 2017. The impact of firework display in the year 2018 was clearly visible in the aerosols optical depth in comparison to that observed in the year 2017. Time averaged aerosol optical depth (AOD) map of India was simulated by incorporating MODIS-Aqua data (https://giova nni. gsfc.nasa. gov/giova nni/) for the sampling period (October 2017 and November 2018). The data incorporated to simulate AOD map were combined Dark Target and Deep Blue product of level-3 atmospheric daily global AOD data (MYD08_D3). The spatial resolution of this data was 1°×1°. Increase in particle levels during AD period could be associated with re-suspension of surface materials (fireworks ash and residuals) through manual brooming activity which also has been reported previously by Tandon et al. (2008). Similar observations for PM2.5 (1501.20 µg m−3) were reported at Bhilai (central part of India) on Diwali (13th November 2012) (Pervez et al. 2015). This study indicated that the short term use of fireworks display potentially degrade air quality and can potentially cause adverse health effects in children and adults over different part of India.