Air pollution has a significant negative impact on various components of ecosystems, human health and ultimately causes significant economic damage. Air pollution is the fourth largest threat to human health, behind high blood pressure, dietary risks and smoking. The aim of the UNECE International Cooperative Program (ICP) Vegetation in the framework of the United Nations Convention on Long-Range Transboundary Air Pollution (CLRTAP) is to identify the main polluted areas of Europe, produce regional maps and further develop the understanding of long-range transboundary pollution. The program is realized in 39 countries of Europe and Asia. Mosses are collected at thousands of sites. Here we present the Data Management System (DMS) of the UNECE ICP Vegetation consisting of a set of interconnected services and tools deployed and hosted at the Joint Institute for Nuclear Research cloud infrastructure. DMS provides its participants with a modern unified system of collecting, analyzing and processing biological monitoring data and facilitates IT aspects of all biological monitoring stages, starting from the choice of sampling sites and finishing with the generation of pollution maps of a particular area or a long-term state-of-environment forecast.
The aim of the UNECE International Cooperative Program (ICP) Vegetation in the
framework of the United Nations Convention on Long-Range Transboundary Air Pollution
(CLRTAP) is to identify the main polluted areas of Europe, produce regional maps, and further
develop the understanding of long-range transboundary pollution. The atmospheric deposition study
of heavy metals, nitrogen, persistent organic compounds (POPs) and radionuclides is based on the
analysis of naturally growing moss samples through surveys carried out every 5 years [
The UNECE ICP Vegetation program had a serious drawback related to its weak adoption of modern informational technologies. Information on collecting and processing samples was carried out manually or with minimum automation. Until 2014, data were mostly stored in Excel files, aggregated manually by the coordinator and preprocessed in specific packages like ArcGIS, MATLAB, etc. Examples of distribution maps from the 2010-2011 Atlas are presented in Figure 1. Files from respondents were usually passed to the coordinator by email. There were no common standards in data transfer, storing and processing software. Such a situation does not meet the modern standards for quality, effectiveness, and speed of research and demands to develop a unified web platform in order to provide a comprehensive solution for biological monitoring and forecasting tasks. Our project was aimed to develop DMS using modern analytical, statistical, programmatic and organizational methods to provide the ICP Vegetation community with a unified system of gathering, storing, analyzing, processing, sharing and using biological monitoring data.
DMS was developed at the Laboratory of Information Technologies of the Joint Institute for
Nuclear Research [
Web portal
Mobile application
Tensorflow
Keras moss.jinr.ru
Real
Model Prediction
Users communicate with DMS through a web portal, a mobile application or web services. We have to deal with four main types of data: sampling sites data, MossMet descriptions, interlaboratory comparison, and POPs. All of them have tens to hundreds of different parameters. To store such variegated data structures we use the non-relational MongoDB. The portal backend was developed with PHP that is also used for calculation of factors, indexes and other statistic parameters. There are two parts of the portal – public and private. General information about the project and the platform is presented in the public part. The private part can be accessed only by authorized contributors and is used for data management and analysis. Data can be imported from and exported to Excel files. Verification of imported data is done automatically and allows finding most of the human-made mistakes. Participants can get some basic and advanced statistic parameters of their data, calculate correlations between element concentrations, contamination factors, geo indexes and so on. We use the OpenLayers library and several backgrounds to generate maps. Data can be represented as a sampling map, a color map (where color represents concentration) and a graduated symbol map. Contributors can share their maps or statistic metrics, so it can be accessed with no credentials. Mostly participants can access only their own data, but in some cases they can use special tools to get general information from other contributors. For example, it is useful to execute the mean values comparison of median elements concentration between neighboring countries and regions (Fig. 3). Contributors can always forbid joint operation with their data at their profile. As soon as we have information for several atlases, it is possible to get historical reports and build historical charts and graphs.
Coordinators of the program can access any contributor’s data, get some general information about completeness and quality of the data, export any type of data, execute group operation and build special combined maps (Fig. 4).
During data processing for the 2015-2016 Atlas, we experienced misspelling of moss names, wrong coordinates, negative concentrations, and many other problems with data. Now for the 20202021 Atlas, we have a mobile application that allows filling in information about sampling sites as required by the UNECE ICP Vegetation manual. The application automatically sets longitude and latitude of the sampling site, controls the correctness of input data and allows capturing photos of moss samples and the nearest area. The application is integrated with DMS, and all information about sampling sites can be imported to the system.
We developed the mobile application using Apache Cordova, so we can build it for Android, iOS, and Windows. Currently, we deployed only the Android version that can be found at Google Play under the “Moss ICP Vegetation” name.
Prediction is an important step in data analysis in any ecological survey. We try to implement
machine learning methods to predict heavy metal concentrations. The general idea is to use data that
we can get from satellite images together with sampling data from DMS to learn a statistical model
or a neural network and then use only data from satellite images to predict concentration. It is clear
that getting indexes from satellite images is a much easier process than field sampling. Sampling and
analysis of an area like the Moscow region can take 4-6 months. Gathering of indexes for the same
area can be done within several days. We use the Google Earth Engine (GEE) [
The study of migration and deposition of toxic pollutants, which include heavy metals, persistent organic pollutants, and radionuclides, and the influence of pollutants on various components of natural and urban ecosystems are key problems of modern biogeochemistry and environmental research. DMS uses modern analytical, statistical, programmatic and organizational methods to supply the ICP Vegetation community with a unified system of collecting, analyzing and processing biological monitoring data. Currently, all key elements of DMS have been implemented. We are going to adjust the functionality of the platform with cluster and factor analysis. A mobile application integrated with DMS was developed for better organization of the samples collection for the 2020–2021 Atlas. The application allows filling in all information about sampling sites required by the UNECE ICP Vegetation manual. Experiments on predicting heavy metal concentrations using machine learning and satellite imagery are in progress. [6] Uzhinskiy, A., Ososkov G., Goncharov P., Frontsyeva M. Combining satellite imagery and machine learning to predict atmospheric heavy metal contamination, Proceedings of the 8th International Conference of Distributed Computing and Grid technologies in Science and Education. CEUR Workshop Proceedings 2267: 351- 358, 2018