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 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 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 Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1770
Author(s):  
Javier González-Enrique ◽  
Juan Jesús Ruiz-Aguilar ◽  
José Antonio Moscoso-López ◽  
Daniel Urda ◽  
Lipika Deka ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Short Term Memory ◽  
Prediction Method ◽  
Monitoring Network ◽  
Exogenous Variables ◽  
Prediction Horizon ◽  
Artificial Neural ◽  
Input Variables ◽  
Analytical Tools

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.

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 Analysis of the distributions of hourly NO<sub>2</sub> concentrations contributing to annual average NO<sub>2</sub> concentrations across the European monitoring network between 2000 and 2014

2017 ◽  
Author(s):  
Christopher S. Malley ◽  
Erika von Schneidemesser ◽  
Sarah Moller ◽  
Christine F. Braban ◽  
W. Kevin Hicks ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Monitoring Network ◽  
Average Concentration ◽  
Monitoring Site ◽  
The European Union ◽  
Annual Average ◽  
Limit Value ◽  
Annual Average Concentration ◽  
Standard Set ◽  
Human Health Effects

Abstract. Exposure to nitrogen dioxide (NO2) is associated with negative human health effects, both for short-term peak concentrations and from long-term exposure to a wider range of NO2 concentrations. For the latter, the European Union has established an air quality limit value of 40 µg m−3 as an annual average. However, factors such as proximity and strength of local emissions, atmospheric chemistry and meteorological conditions means that there is substantial variation in the hourly NO2 concentrations contributing to an annual average concentration. The aim of this analysis was to quantify the nature of this variation at thousands of monitoring sites across Europe through the calculation of a standard set of chemical climatology statistics. Specifically, at each monitoring site that satisfied data capture criteria for inclusion in this analysis, annual NO2 concentrations, as well as the percentage contribution from each month, hour of the day, and hourly NO2 concentrations divided into 5 µg m−3 bins were calculated. Across Europe, 2010–2014 average annual NO2 concentrations (NO2AA) exceeded the annual NO2 limit value at 8 % of > 2500 monitoring sites. The application of this chemical climatology approach showed that sites with distinct monthly, hour of day, and hourly NO2 concentration bin contributions to NO2AA were not grouped in specific regions of Europe, and within relatively small geographic regions there were sites with similar NO2AA, but with differences in these contributions. Specifically, at sites with highest NO2AA, there were generally similar contributions from across the year, but there were also differences in the contribution of peak vs moderate hourly NO2 concentrations to NO2AA, and from different hours across the day. Trends between 2000 and 2014 for 259 sites indicate that, in general, the contribution to NO2AA from winter months has increased, as has the contribution from the rush-hour periods of the day, while the contribution from peak hourly NO2 concentrations has decreased. The variety of monthly, hour of day and hourly NO2 contribution bin contributions to NO2AA, across cities, countries and regions of Europe indicate that within relatively small geographic areas different interactions between emissions, atmospheric chemistry and meteorology produce variation in NO2AA and the conditions that produce it. Therefore, measures implemented to reduce NO2AA in one location may not be as effective in others. The development of strategies to reduce NO2AA for an area should consider i) the variation in monthly, hour of day and hourly NO2 concentration bin contributions to NO2AA within that area, and ii) how specific mitigation actions will affect variability in hourly NO2 concentrations.

scholarly journals Estimating daily surface NO<sub>2</sub> concentrations from satellite data - A case study over Hong Kong using land use regression models

2016 ◽  
Author(s):  
Jasdeep S Anand ◽  
Paul S Monks
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Satellite Data ◽  
Monitoring Network ◽  
Fine Scale ◽  
Land Use Regression ◽  
Vertical Column ◽  
Mixed Effect ◽  
In Situ Data

Abstract. Land Use Regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during 2005-2015. In-situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in-situ data shows that the mixed effect LUR model using OMI data has a high predictive power (adj. R2 = 0.84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0.11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

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