Модель дисперсии индустриального загрязнения воздуха AERMOD уточняется с помощью наблюдений дистанционного зондирования.Оценивается эффект от привлечения специфичных для каждой территории данных по альбедо, шероховатости поверхности и коэффициенту Боуэна вместо глобальных стандартов AERMOD, а для необходимой для оценки этих параметров классификации землепользования -эффект от привлечения космических данных вместо имеющихся в свободном доступе глобальных карт.В настоящей части работы даётся обзор исследований по извлечению указанных параметров из космических данных, использованию их в AERMOD и влиянию на модельные концентрации.Далее описаны исходные данные для исследования этого вопроса в настоящей работе на материале пяти реальных предприятий и способы измерения эффекта привлечения космических данных.Эффект измеряется тремя способами: а) как разность между годовыми максимумами часовых концентраций критичного загрязнителя («абсолютный максимум»); б) то же с ограничением дневными рабочими часами и использованием 95%-го квантиля вместо абсолютного максимума («регуляторный критерий»); в) как максимальная почасовая разница за год («мгновенный критерий»

Модель дисперсии индустриального загрязнения воздуха AERMOD уточняется с помощью наблюдений дистанционного зондирования.Настоящая статья сфокусирована на трёх параметрах поверхности, влияющих на рассеяние загрязнений (альбедо, шероховатость поверхности и коэффициент Боуэна).Альбедо и шероховатость поверхности оцениваются по дистанционным наблюдениям напрямую, а коэффициент Боуэна требует косвенных оценок тепловых потоков или использования включающих их климатических моделей.Оценивается эффект от привлечения этих новых данных, специфичных для каждой территории, вместо глобальных стандартов AERMOD.Эффект уточнения параметров поверхности рассматривается для трёх реальных предприятий и измеряется тремя способами: а) как разность между годовыми максимумами часовых концентраций критичного загрязнителя («абсолютный максимум»); б) то же с ограничением дневными рабочими часами и использованием 95%-го квантиля вместо абсолютного максимума («регуляторный критерий»); в) как максимальная почасовая разница за год («мгновенный критерий»).Значения этих критериев делятся либо на референтную концентрацию загрязнителя, определяющую предельную допустимую его концентрацию, что даёт меру влияния на оценку острого риска, либо на концентрацию по стандартам AERMOD, что даёт относительную меру влияния космических данных.Для первого критерия преобладает влияние шероховатости, а влияние альбедо и параметра Боуэна мало.Для второго влияние шероховатости менее заметно, а влияние альбедо и параметра Боуэна значительно

In this paper, variants of the theoretical formalism are presented, which allow one to quantify the information flows in the optimal control system. The fundamental role of feedback is substantiated: observation ↔ control: information circulation through the feedback loop, when the information embedded in the object through its control, at the next time step, returns to the controlling subject in the form of information contained in observations of the object.

We describe a method for recognition of template objects in multi-and hyperspectral remote sensing data.The matrices of correlation between spectral channels are compared to correlation matrices of templates.The correlation between these matrices is a measure of similarity (double correlation, DC).Templates are recognized in data using the maximum of DC between a template and a fragment of data.The method is sensitive to spatial variations within the fragment as a complement to maximum likelihood (ML) method of classification based on averaged spectra.We add DC to ML in classification of multitemporal Landsat data stacked like a hyperspectral cube for surface types important for air pollution dispersion.For three surface targets for DC (industrial, dense residential and low intensity residential), similar spectrally but different spatially, the effect of DC measured by improvement of the sum of missed target and false alarm probabilities is 2% -14%.

Conceptual system developed in optimal control theory for technical purposes is used as a philosophical instrument applied to cyclic information processes, which are expected to be the basis of noosphere. Noosphere was perceived by the founding fathers of this concept, Vernadsky, Teilhard de Chardin, e.a. as an outgrowth of the evolutionary process, which begins with cosmogenesis and proceeds through geosphere and biosphere. We attempt to apply the optimal control concepts to all three levels - geospheric, biospheric, and noospheric - due to their having a common structure of information processes (or entropic processes considered as proto- information). These processes include homeostasis, accumulation and expenditure of information, formation of hierarchical information structures, evolution involving the breaks of homeostasis etc. In noosphere, controlled system may have the same informational capabilities as controlling system, so that the term "dialog" is more adequate; in this case, we extend optimal control description to game theory. The cyclic, feedback logic of optimal control seems better adapted to noospheric processes than usual cause-effect logic. This second part of the paper proceeds from the geo- and biospheric levels discussed in the first part to the noospheric level. The basic structure at this level is the fusion of natural matter/energy cycles characteristic for geosphere with anthropogenic information cycles, which extend information accumulation and adaptation inherited from biospheric level into reflective realm. The basic type of informational interaction between these structures is construed in perspective of game theory between reflective players. Its essential feature is the interaction between reflective images that each player forms of other players and of oneself. We describe the nontrivial information flows that can arise in a distributed global system of such structures, including complex interactions between collective and individual levels and paradoxes. We analyze the role of science as the carrier of the "rational model", on which this entire system is based, and the impact on this model of the sociobiological background of science inherited from the biospheric level. We also discuss the role of natural language as an alternative noospheric structure capable of supporting the irrational components of the noosphere. City is discussed as an example of an emerging structure integrating the basic noospheric components, albeit in an inchoate form. Finally, we consider the reflective identities ("I"), which may emerge in noosphere, and the relevant ethical issues.

This study investigated to what extent risk estimates can be modified by involving in the process readily available space data and well-tested processing methods. We classify Landsat data for these types using the support vector algorithm and small characteristic training sites for each type. The dispersion modeling problem, unlike most classification tasks, is tolerant of unclassified areas. We show that the classification obtained is better than available global maps based on MODIS or Landsat. For a large chemical plant, we perform dispersion modeling and calculate the maximal hourly concentrations and acute risk from the main pollutant. We compare several versions of calculated risk based on the surface parameters assessed from global maps and variants of Landsat classification to show that the latter are twice as accurate as the former (with a 20 and 40% error, respectively). Risk estimates are shown to vary considerably (by 25%) depending on the yearly set of Landsat data used, so that using multi-year data is a must, unless land use changes considerably over the period. Thus, in assessing the hazard from air pollution from any specific plant, which is an obligatory procedure for establishing the plant's sanitary protection zone and obtaining the pollutant emission permit, it is desirable to use Landsat data.

The conceptual system developed in optimal control theory for technical purposes is used as a philosophical instrument applied to cyclic information processes, which are expected to be the basis of noosphere. Noosphere was perceived by the founding fathers of this concept, Vladimir Vernadsky, Pierre Teilhard de Chardin, e.a. as an outgrowth of the evolutionary process, which begins with cosmogenesis and proceeds through geosphere and biosphere. We attempt to apply the optimal control concepts to all three levels - geospheric, biospheric, and noospheric - due to their having a common structure of information processes (or entropic processes considered as proto-information). These processes include homeostasis, accumulation and expenditure of information, formation of hierarchical information structures, evolution involving the breaks of homeostasis etc. In noosphere, controlled system may have the same informational capabilities as controlling system, so that the term "dialog" is more adequate; in this case, we extend optimal control description to game theory. The cyclic, feedback logic of optimal control seems better adapted to noospheric processes than usual cause-eff ect logic. The first part of the paper considers the geospheric and biospheric level. We introduce the basic notions characterizing optimal control cycle: duality of observation and control, hierarchy of models, active sounding, balance of information inflow and outflow, optimized criterion, networked (distributed) control, etc. Then, natural homeostases at the geospheric level are considered as a form of self-regulation having specific optimized criteria. The constitutive feature of this level is the absence of information processing in the strict sense: its place is taken by entropic processes. Therefore, no goal can exist at this level, and we consider it as a part of cosmogenesis, which is allegedly goalless/meaningless. We discuss the anthropic principle as a means to overcome this limitation and its possible impact on understanding of geosphere. Next, we consider the biospheric level as one with genetic information accumulation but without reflection. Interaction between genetic and phenetic structures is described in optimal control terms. Phylogenesis is described as restructuring of genetic "models", and the problem of origin of life is considered as a specific case of information paradox called "loan from the future". We consider also the Gaia concept of biosphere regulating the geosphere and express it in optimal control language.

Intermittent irregular sources of air pollution, when used in dispersion modeling, can exaggerate the acute health risk due to improbable coincidence of release with the worst-case meteorological conditions. This problem is alleviated by randomizing the moments of emission and applying the Monte Carlo method to obtain the realistic expected yearly maxima of hourly concentrations/risks. Emissions are modeled as irregular “pulses,” possibly with additional constraints on timing. Such are major emission sources in important industries: oil refineries, gas extraction, cement production, etc. We have tested the approach in ~100 projects for industrial plants in Russia and obtained considerable reductions in estimated acute health risks: up to two orders of magnitude, depending on the level of intermittency of sources. These corrections to unrealistically high worst-case concentration values at nearby populated areas are, in many cases, a key to obtaining reasonable exclusion/protection zones for plants. To our knowledge, such a body of results on intermittent irregular sources is unique, and it can be useful especially for developing countries where the exact timeline of emissions is often unknown. Taking intermittency into account is also known to be an important step toward compliance with 1-h US National Ambient Air Quality Standard. We provide a detailed description of the Monte Carlo algorithm used. We compare Monte Carlo with the usual quantile-based approach to peak values; they agree when quantile is dependent on intermittency. The non-linearity of maximum function gives rise to some counterintuitive phenomena, which call for refinement of risk definition for intermittent irregular sources.

We try to identify the unknown source of air pollution caused by unknown substance. We do it using only complaints on odor submitted by population living in the ∼20 × 30-km area. We test all possible locations of pollution source on the grid with 1-km spacing covering the area. For each virtual location, we perform the dispersion modeling using AERMOD with meteorological data from three nearest stations. We assume a low, ambient temperature area source with unit emission rate. The space-time array of concentrations is correlated with the analogous array of complaints, and the best correlated source position is chosen. We performed extensive experimentation with data processing options to obtain the best spatially focused correlation hotspot. This included filtering the moments of time used for correlation, quantizing the complaint numbers and concentrations, and finding the best time lag between concentrations and complaints. The best lag was 3 h in total or 1 h added to propagation time from source to receptor. The resulting focus is 1–2 km wide, positioned exactly on an active landfill. We estimate this accuracy as very good, better than warranted by the quality of source data. This demonstrates the possibility of obtaining a good focus for the location of an unknown source even from very imperfect data. The technique applied can be useful in many situations where the inventory of air pollution sources is incomplete, especially in developing countries.

По шлейфам, образованным на снимках Landsat факелами отдувок газовых скважин, определялась концентрация сажи и через нее – расход газа в продувке. Кроме того, определялись параметры атмосферы – скорость ветра, устойчивость, – от которых зависит форма шлейфа. Основой служила минимизация энтропийного критерия невязки между наблюдениями шлейфа и его расчетной формой и оптической плотностью, которые генерировались моделью рассеяния ISC3ST в зависимости от параметров атмосферы и источника выброса. Согласие модели и данных достаточно хорошее и оцененные таким образом параметры вполне согласуются с данными предприятия об источнике и метеоусловиях.

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTAcute Health Risk from Irregular Intermittent Air Pollution Sources: Challenges of DefinitionBoris Balter*† and Marina Faminskaya†‡View Author Information† Space Research Institute, Russian Academy of Sciences, Moscow 117997, Russia‡ Russian State Social University, Moscow 129226, Russia*E-mail: [email protected]Cite this: Environ. Sci. Technol. 2014, 48, 24, 14070–14071Publication Date (Web):November 20, 2014Publication History Received25 September 2014Published online20 November 2014Published inissue 16 December 2014https://pubs.acs.org/doi/10.1021/es504712xhttps://doi.org/10.1021/es504712xnewsACS PublicationsCopyright © 2014 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views983Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (2 MB) Get e-AlertscloseSUBJECTS:Aggregation,Environmental modeling,Mathematical methods,Receptors,Wind Get e-Alerts

Излагается метод коррекции параметров модели удельной эффективной площади рассеяния (УЭПР) озимой пшеницы с использованием данных авиационного гиперспектрального (ГС) зондирования, а также данных измерений in situ. На основе вычисленных значений коэффициента спектральной яркости (КСЯ) и спектрального индекса MTVI2 строятся поля оценок индекса листовой поверхности (LAI). Эти поля используются для уточнения параметров модели УЭПР. Проводится сравнение результатов зависимости от УЭПР, предсказанных на основе предложенной модели УЭПР и данных наземных измерений КСЯ и УЭПР. Используется формализм фильтрации Калмана для оценки текущих значений индекса листовой поверхности (LAI) и параметров модели УЭПР озимой пшеницы.

Исследуются методы тематической обработки данных авиационного гиперспектрального зондирования и сенсора ИСЗ Quickbird. Приводится описание данных дистанционного зондирования почвенно-растительных объектов на тестовом полигоне и на трассе полета вертолета – носителя авиационного гиперспектрометра. Для исследования были выбраны три категории объектов: целевой растительный объект, фоновый растительный объект и почва. Использовались методы кластерного анализа данных на базе нейросети Кохонена и максимума правдоподобия. Показано, что данные гиперспектрометра обеспечивают получение более устойчивых результатов распознавания по отношению к выбору обучающих участков, чем данные сканера.

Излагаются методические вопросы имитационного моделирования РСА-изображений земной поверхности с помощью программного комплекса “Геодиалог”. Описаны модели зондируемых природных и антропогенных объектов. Рассмотрены процессы рассеяния электромагнитных волн статистически шероховатыми пространственно неоднородными поверхностями с учетом поляризации зондирующего и принимаемого излучения. При моделировании РСА-изображений учтены процеду-ры синтеза апертуры и влияния на них спекл-шума.

Описан проект гиперспектрометра, предназначенного для установки на малый космический аппарат (МКА). Представлены структурная и габаритная схемы прибора и его основные технические характеристики. Дается краткое описание космической платформы МКА “Астрогон–Вулкан” в качестве одного из возможных вариантов размещения гиперспектрометра и методов обработки гиперспектральной информации. Приводится описание научных и прикладных задач, эффективное решение которых реализуется с помощью гиперспектральных данных.