scholarly journals Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique

2021 ◽  
Vol 13 (20) ◽  
pp. 4055
Author(s):  
Jian Guan ◽  
Bohan Jin ◽  
Yizhe Ding ◽  
Wen Wang ◽  
Guoxiang Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Confidence Intervals ◽  
Amazon Basin ◽  
Surface Concentration ◽  
Average Concentration ◽  
Monitoring Networks ◽  
Vertical Column ◽  
Atmospheric Pollutant ◽  
Surface Distribution ◽  
Global Surface

Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants in outdoor air. However, the lack of monitoring of the global surface concentration of HCHO is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or, for research on a global scale, too data-demanding. To alleviate this issue, we adopted neural networks to estimate the 2019 global surface HCHO concentration with confidence intervals, utilizing HCHO vertical column density data from TROPOMI, and in-situ data from HAPs (harmful air pollutants) monitoring networks and the ATom mission. Our results show that the global surface HCHO average concentration is 2.30 μg/m3. Furthermore, in terms of regions, the concentrations in the Amazon Basin, Northern China, South-east Asia, the Bay of Bengal, and Central and Western Africa are among the highest. The results from our study provide the first dataset on global surface HCHO concentration. In addition, the derived confidence intervals of surface HCHO concentration add an extra layer of confidence to our results. As a pioneering work in adopting confidence interval estimation to AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper paves the way for rigorous study of global ambient HCHO health risk and economic loss, thus providing a basis for pollution control policies worldwide.

scholarly journals Global Surface HCHO Distribution derived from Satellite Observations with Neural Networks Technique

2021 ◽  
Author(s):  
Jian Guan ◽  
Bohan Jin ◽  
Yizhe Ding ◽  
Wen Wang ◽  
Guoxiang Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Confidence Interval ◽  
Amazon Basin ◽  
Monitoring Network ◽  
Surface Concentration ◽  
Average Concentration ◽  
Vertical Column ◽  
Atmospheric Pollutant ◽  
Surface Distribution ◽  
Global Surface

Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants. However, the lack of global surface concentration of HCHO monitoring is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or data- demanding for a global scale of research. To alleviate this issue, we adopted neural networks to estimate surface HCHO concentration with confidence intervals in 2019, where HCHO vertical column density data from TROPOMI, in-situ data from HAPs (harmful air pollutants) monitoring network and ATom mission are utilized. Our result shows that the global surface HCHO average concentration is 2.30 μg/m3. Furthermore, in terms of regions, the concentration in Amazon Basin, Northern China, South-east Asia, Bay of Bengal, Central and Western Africa are among the highest. The results from our study provides a first dataset of the global surface HCHO concentration. In addition, the derived confidence interval of surface HCHO concentration adds an extra layer for the confidence to our results. As a pioneer work in adopting confidence interval estimation into AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper will pave the way for the rigorous study on global ambient HCHO health risk and economic loss, thus providing a basis for pollutant controlling policies worldwide.

scholarly journals Mapping Global Surface HCHO Distribution with Confidential Interval by Satellite Observation

2021 ◽  
Author(s):  
Bohan Jin ◽  
Jian Guan ◽  
Yizhe Ding ◽  
Wen Wang ◽  
Guoxiang Li
Keyword(s):  
Amazon Basin ◽  
Interval Estimation ◽  
Monitoring Network ◽  
Surface Concentration ◽  
Satellite Observation ◽  
Average Concentration ◽  
Vertical Column ◽  
Atmospheric Pollutant ◽  
Surface Distribution ◽  
Global Surface

Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants. However, the lack of global surface concentration of HCHO monitoring is currently hindering researches on outdoor HCHO pollution. Traditional methods are either too naïve or data-demanding for a global scale research. To alleviate this issue, we trained two fully-connected neural networks respectively for deriving point and interval estimation of surface HCHO concentration in 2019, where vertical column density data from TROPOMI, in-situ data from HAPs (harmful air pollutants) monitoring network and ATom mission are utilized. Our result shows that the global surface HCHO average concentration is 2.30 μg/m3. Furthermore, in terms of regions, the concentration in Amazon Basin, North China, South-east Asia, Bay of Bengal, Central and Western Africa are among the highest. Our study makes up for the global shortage of surface HCHO monitoring and helps people have a clearer understanding of surface concentration distribution of HCHO. In addition, with the help of quality-driven algorithm, interval estimation of surface HCHO concentration is believed to bring confidence to our results. As an early work adopting interval estimation in AI-driven atmospheric pollutant research and the first to map global HCHO surface distribution, our paper will pave way for rigorous study on global ambient HCHO health risk and economic loss, thus providing basis for pollutant controlling policies worldwide.

scholarly journals Application of Neural Networks for Retrieval of the CO2 Concentration at Aerospace Sensing by IPDA-DIAL lidar

2019 ◽  
Vol 11 (6) ◽  
pp. 659 ◽  
Author(s):  
Gennadii G Matvienko ◽  
and Alexander Ya Sukhanov
Keyword(s):  
Neural Networks ◽  
Co2 Concentration ◽  
Surface Concentration ◽  
Average Concentration ◽  
Differential Absorption ◽  
Near Surface ◽  
Sources And Sinks ◽  
Pressure Profiles ◽  
Temperature And Pressure ◽  
Fully Connected

Greenhouse gas concentrations are increasing over the past few decades, creating the need to measure their concentration with high accuracy, including for determining their trends, sources, and sinks. In this regard, various methods of regional and global control are being developed. One of the measuring methods is passive satellite method, but they allow for you to get data mainly during the day and outside the poles of the Earth. Another method is active lidar; they require the consideration of various aspects that are related to the technical characteristics of the lidar and methods for solving inverse problems. This article discusses the possibility of using lidars for sensing carbon dioxide from space (orbit 450 km) and from a height of 10 km and 23 km, which presumably corresponds to the aircrafts and balloons. As a method of solving the inverse problem, the method of fully connected neural networks with three layers and pre-training of first layer is considered, allowing for the application of additional data, including the IPDA (Integrated Path Differential Absorption) signal, the scattered DIAL (Differential Absorption Lidar) signal, temperature, and pressure profiles. These estimates show the possibility of measuring the average concentration from an orbit height of 450 km with an error of 0.16%, a resolution of 60 km, with a 50 mJ laser pulse energy, and 1 m diameter telescope. It is also shown that it is possible to obtain the concentration profile, including the near-surface concentration with an error of 2 ppm.

scholarly journals Atmospheric Pollutant Flow and Precipitation: Modeling Effects on the Vegetation Ecosystem

2018 ◽  
Vol 7 (3) ◽  
pp. 157-161
Author(s):  
Allag Fateh ◽  
Saddek Bouharati ◽  
Lamri Tedjar ◽  
Mohamed Fenni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Wide Distribution ◽  
Climatic Conditions ◽  
Mathematical Methods ◽  
Atmospheric Pollutant ◽  
Artificial Neural ◽  
Precipitation Modeling ◽  
The Impact

Because of their fixed life and wide distribution, plants are the first victims of air pollution. The atmosphere is considered polluted when the increase of the rate of certain components causes harmful effects on the different constituents of the ecosystems. The study of the flow of air near a polluting source (cement plant in our case), allows to predict its impact on the surrounding plant ecosystem. Different factors are to be considered. The chemical composition of the air, the climatic conditions, and the impacted plant species are complex parameters to be analyzed using conventional mathematical methods. In this study, we propose a system based on artificial neural networks. Since artificial neural networks have the capacity to treat different complex parameters, their application in this domain is adequate. The proposed system makes it possible to match the input and output spaces. The variables that constitute the input space are the chemical composition, the concentration of the latter in the rainwater, their duration of deposition on the leaves and stems, the climatic conditions characterizing the environment, as well as the species of plant studied. The output variable expresses the rate of degradation of this species under the effect of pollution. Learning the system makes it possible to establish the transfer function and thus predict the impact of pollutants on the vegetation.

scholarly journals Transport of Saharan dust from the Bodélé Depression to the Amazon Basin: a case study

2010 ◽  
Vol 10 (2) ◽  
pp. 4345-4372 ◽  
Author(s):  
Y. Ben-Ami ◽  
I. Koren ◽  
Y. Rudich ◽  
P. Artaxo ◽  
S. T. Martin ◽  
...  
Keyword(s):  
Atlantic Ocean ◽  
Amazon Basin ◽  
Marine Boundary Layer ◽  
Average Concentration ◽  
Saharan Dust ◽  
Aerosol Layer ◽  
Amazon Forest ◽  
Close Link ◽  
Bodele Depression

Abstract. Through long-range transport of dust, the Sahara desert supplies essential minerals to the Amazon rain forest. Since Saharan dust reaches South America mostly during the Northern Hemisphere winter, the dust sources active during winter are the main contributors to the forest. Given that the Bodélé depression area in Southwestern Chad is the main winter dust source, a close link is expected between the Bodélé emission patterns and volumes and the mineral supply flux to the Amazon. Until now, the particular link between the Bodélé and the Amazon forest was based on sparse satellite measurements and modeling studies. In this study, we combine a detailed analysis of space-borne and ground data with reanalysis model data and surface measurements taken in the Central Amazon during the Amazonian Aerosol Characterization Experiment (AMAZE-08) in order to explore the validity and the nature of the proposed link between the Bodélé depression and the Amazon forest. This case study follows the dust events of 11–16 and 18–27 February 2008, from the emission in the Bodélé over West Africa, the crossing of the Atlantic Ocean, to the observed effects above the Amazon canopy about 10 days after the emission. The dust was lifted by surface winds stronger than 14 m s−1, usually starting early in the morning. The lofted dust mixed with biomass burning aerosols over Nigeria, was transported over the Atlantic Ocean, and arrived over the South American continent. The top of the aerosol layer reached above 3 km, and the bottom merged with the marine boundary layer. The arrival of the dusty air parcel over the Amazon forest increased the average concentration of aerosol crustal elements by an order of magnitude.

scholarly journals Retrieval of total column and surface NO<sub>2</sub> from Pandora zenith-sky measurements

2019 ◽  
Author(s):  
Xiaoyi Zhao ◽  
Debora Griffin ◽  
Vitali Fioletov ◽  
Chris McLinden ◽  
Jonathan Davies ◽  
...  
Keyword(s):  
Transport Model ◽  
Surface Concentration ◽  
In Situ Measurements ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Chemical Transport Model ◽  
High Quality ◽  
Vertical Column

Abstract. Pandora spectrometers can retrieve nitrogen dioxide (NO2) vertical column densities (VCDs) via two viewing geometries: direct-sun and zenith-sky. The direct-sun NO2 VCD measurements have high quality (0.1 DU accuracy in clear-sky conditions) and do not rely on any radiative transfer model to calculate air mass factors (AMFs); however, they are not available when the sun is obscured by clouds. To perform NO2 measurements in cloudy conditions, a simple but robust NO2 retrieval algorithm is developed for Pandora zenith-sky measurements. This algorithm derives empirical zenith-sky NO2 AMFs from coincident high-quality direct-sun NO2 observations. Moreover, the retrieved Pandora zenith-sky NO2 VCD data are converted to surface NO2 concentrations with a scaling algorithm that uses chemical-transport-model predictions and satellite measurements as inputs. NO2 VCDs and surface concentrations are retrieved from Pandora zenith-sky measurements made in Toronto, Canada, from 2015 to 2017. The retrieved Pandora zenith-sky NO2 data (VCD and surface concentration) show good agreement with both satellite and in situ measurements. The diurnal and seasonal variations of derived Pandora zenith-sky surface NO2 data also agree well with in situ measurements (diurnal difference within ±2 ppbv). Overall, this work shows that the new Pandora zenith-sky NO2 products have the potential to be used in various applications such as future satellite validation in moderate cloudy scenes and air quality monitoring.

Development of artificial neural networks based confidence intervals and response surfaces for the optimization of coagulation performance

2015 ◽  
Vol 15 (5) ◽  
pp. 1079-1087 ◽  
Author(s):  
Robert H. McArthur ◽  
Robert C. Andrews
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Confidence Intervals ◽  
Response Surfaces ◽  
Average Interval ◽  
Squared Error ◽  
Interval Width ◽  
Artificial Neural

Effective coagulation is essential to achieving drinking water treatment objectives when considering surface water. To minimize settled water turbidity, artificial neural networks (ANNs) have been adopted to predict optimum alum and carbon dioxide dosages at the Elgin Area Water Treatment Plant. ANNs were applied to predict both optimum carbon dioxide and alum dosages with correlation (R2) values of 0.68 and 0.90, respectively. ANNs were also used to developed surface response plots to ease optimum selection of dosage. Trained ANNs were used to predict turbidity outcomes for a range of alum and carbon dioxide dosages and these were compared to historical data. Point-wise confidence intervals were obtained based on error and squared error values during the training process. The probability of the true value falling within the predicted interval ranged from 0.25 to 0.81 and the average interval width ranged from 0.15 to 0.62 NTU. Training an ANN using the squared error produced a larger average interval width, but better probability of a true prediction interval.

scholarly journals Tropospheric NO<sub>2</sub> column densities deduced from zenith-sky DOAS measurements in Shanghai, China, and their application to satellite validation

2008 ◽  
Vol 8 (4) ◽  
pp. 16713-16762 ◽  
Author(s):  
D. Chen ◽  
B. Zhou ◽  
S. Beirle ◽  
L. M. Chen ◽  
T. Wagner
Keyword(s):  
Solar Zenith Angle ◽  
Surface Concentration ◽  
Urban Site ◽  
Optical Absorption Spectroscopy ◽  
Vertical Column ◽  
Satellite Validation ◽  
Tropospheric No2 ◽  
Realistic Information ◽  
Good Agreement

Abstract. Zenith-sky scattered sunlight observations using differential optical absorption spectroscopy (DOAS) technique were carried out in Shanghai, China (31.3° N, 121.5° E) since December 2006. At this polluted urban site, the measurement provided NO2 total columns in the daytime. Here, we present a new method to extract time series of tropospheric vertical column densities (VCD) of NO2 from these observations. The derived tropospheric NO2 VCD is an important quantity for the estimation of emissions and for the validation of satellite observations. Our method makes use of assumptions on the relative NO2 height profiles and on the diurnal variation of the stratospheric NO2 VCD. The influence of these parameters on the retrieved tropospheric NO2 VCD is discussed; for a polluted site like Shanghai, the accuracy of our method is estimated to be <20% for solar zenith angle (SZA) lower than 85°. From simultaneously performed long-path DOAS measurement, the NO2 surface concentration at the same site was observed and the corresponding tropospheric NO2 VCD was estimated using the assumed seasonal NO2 profiles in the planetary boundary layer (PBL). By making a comparison between the tropospheric NO2 VCD from zenith-sky and long-path DOAS measurements, it was found that the former provided more realistic information about total tropospheric pollution than the latter, so it's more suitable for satellite data validation than the in situ measurement. A comparison between the tropospheric NO2 VCD from ground-based zenith-sky measurement and SCIAMACHY was also made. Satellite validation for a strongly polluted area is highly needed, but exhibits also a great challenge. Our comparison showed good agreement, considering in particular the different spatial resolutions between the two measurements.

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