Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Abstract

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.

Notes on environmental forecasting in the current conditions

“…In the case of a shortage of measurement data, it is advisable to use methods for solving inverse continuation problems, given that mathematical models in the data assimilation mode act as tools for interpolation or extrapolation of calculation data or observational data. The inverse problems of data assimilation are formulated as interrelated problems at specified intervals defined as windows of assimilation [28][29][30] . Methods of sensitivity theory work through estimates of variations of generalized functionals (or target functionals of the environment quality) to perturbations of system parameters for the entire complex of models, for individual fragments of models, etc.…”

“…They enable a constructive approach to interactive analysis and solving environmental quality management problems. Optimization of monitoring systems and evaluation of the informative value of monitoring data are also important tasks of inverse modeling 30 .…”

“…Since the 70s of the last century, ICM&MG SB RAS has accumulated extensive experience in developing its own models for solving various environmental problems [26][27][28][29][30] . We are developing general approaches that allow us to work uniformly with a wide range of forward and inverse modeling problems.…”

Random Forests Assessment of the Role of Atmospheric Circulation in PM10 in an Urban Area with Complex Topography

“…Exposure to ambient PM concentration with a diameter below 10 µm (PM 10 ) increases the possibility of preterm birth [5], deaths from respiratory disease [6] and also causes lung irritation, cellular damage, coughing asthma, and cardiovascular diseases [7]. High PM concentrations in urbanized areas are the consequence of the interaction of many factors, including anthropogenic and natural sources of air pollution, chemical and physical reactions between primary and secondary pollutants, and dispersion conditions determined by atmospheric circulation types, meteorological conditions, and meso-and microclimatic features of the analyzed area [1,8,9]. Numerous studies confirm that atmospheric circulation is an important factor determining the level of air pollution in the lower troposphere, especially in urbanized and industrial areas, which are characterized by elevated pollution emissions [3,[10][11][12][13].…”

Численный Эксперимент По Оценке Источников Загрязнения Атмосферы На Основе Данных Корабельной Экспедиции И Решения Обратной Задачи

“…источники рассматривались для веществ NO, NO2, участвующих в химической трансформации и влияющих на изменение концентрации третьего вещества (O3) (на примере азот-озонового цикла[2] на основе соотношений Лейтона). При этом концентрации O3 является измеряемыми, а NO, NO2 используются для проверки.Для решения задачи идентификации источника использовался алгоритм из[3,4]. Идея подхода состоит в преобразовании обратной задачи в семейство квазилинейных операторных уравнений с операторами чувствительности.…”