تخمین غلظت ذرات معلق با استفاده از روش‌های رگرسیون و شبکه عصبی از داده‌های ماهواره‌ای اسپکترورادیومتر تصویربردار چندزاویه‌ای (misr) در شهر تهران

از اثرات عمده تغییر اقلیم، تاثیر آن بر کیفیت محصولات کشاورزی می‌باشد و انگور یکی از محصولات باغی استراتژیک کشاورزی می‌باشد. مقادیر دما و بارش روزانه ایستگاه گلمکان براساس مدل hadcm3 در دوره پایه (2005-1987) و آینده نزدیک (2050-2020) تحت سناریوهای rcp8.5,rcp4.5 با استفاده از روش ﻋﺎﻣﻞ ﺗﻐﻴﻴﺮ، ریزمقیاس شدند سپس با استفاده از سه سری داده‌های پایه هواشناسی، ریزمقیاس نمایی و کیفیت مشاهداتی انگور، کیفیت انگور برای آینده با بکارگیری شبکه عصبی پرسپترون در matlab 2019a ﺷﺒﯿﻪ ﺳﺎزی شده است. مدل اقلیمی، اﻓﺰاﯾﺶ دﻣﺎ و ﮐﺎﻫﺶ ﺑﺎرﻧﺪﮔﯽ در آینده را تحت سناریوهای rcp8.5,rcp4.5 نسبت ﺑﻪ دوره ﭘﺎﯾﻪ نشان داد. دﻣﺎی حداکثر به ترتیب 3، 9 و 4.7 درجه سانتی گراد افزایش و دﻣﺎی حداقل به ترتیب 3.8 و 4.4 درجه سانتی گراد افزایش و ﺑﺎرش به ترتیب 0.3 و 0.8 میلیمتر کاهش را دارد. هر یک از متغیرهای مستقل دمای کمینه، بیشینه، و بارش با هر یک از متغیرهای وابسته سن درخت، قند، وزن خوشه، اندازه خوشه، طول میوه، عرض میوه، اسیدیته، ph و tss رابطه معناداری را بر پایه آزمون پیرسون نشان می‌دهند. تحت هر دو سناریو وزن خوشه، اندازه خوشه، طول میوه، عرض میوه، قند، ph، tss بریکس، اسیدیته و وزن حبه به صورت کاهشی پیش بینی می‌شود. در rcp8.5 میزان تغییرات بیشتر از rcp4.5 می‌باشد. در خصوصیات رنگ آبمیوه، رنگ گوشت، طعم میوه، انبارداری، بازارپسندی و حمل و نقل در دو سناریو بدون تغییر است. آزمون ttest تغییر در متغیرهای ph، قند، اسیدیته، وزن خوشه، طول میوه و طول در عرض خوشه در دو سناریو معنادار بوده است. متغیرهای وزن حبه و عرض میوه در دو سناریو 4.5 و 8.5، اندازه خوشه سناریو 8.5 و طول در عرض حبه سناریوی 4.5 فاقد تغییرات معنی داری است. نتایج نشان می‌دهد، دراثر افزایش دما و کاهش بارندگی در اقلیم آتی، برخی متغیرهای کیفت انگور در آینده با روند کاهش معنی داری مواجه خواهند شد.

Retrieving concentration of particle matters using regression models and neural network model from Multi-angle Imaging SpectroRadiometer (MISR) satellite data in Tehran

Aerosols or airborne particulate matter (PM) with different sizes from both natural and anthropogenic emission sources have substantial influences on climate, environment and human health. Particulate matter is characterized by the size (PM10 andPM2.5; particles having an aerodynamic diameter less than 10 and 2.5 μm). However, due to limited spatial coverage and high operational cost, in situ observations are insufficient to capture high resolution, tempospatial variation of PM concentration, especially for many developing countries such as Iran. Satellite remote sensing technology, on the other hand, provides a costeffective way for epidemiological studies and PM monitoring. at various scales by measuring satellitederived aerosol optical depth (AOD), especially for places where groundlevel monitoring is not available. Satellitederived AOD is related to groundlevel PM concentration and can be empirically converted into PM mass. Therefore, a number of empirical models have been developed to predict groundlevel PM2.5 or PM10 concentration from various satellitederived AOD products, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectro Radiometer (MISR). There are a few studies about the AODPM relationship in Iran and much fewer studies use MISR instrument.  level2 product of AOD retrieved by MISR with improved resolution of 4.4 km, is a suitable instrument for estimating particular matter. Therefore, the main aim of this study was investigating empirical linear and nonlinear regression models along with artificial neural network model for estimating particulate matter (containing PM2.5 and PM10) using AOD from the MISR 4.4 km aerosol product. In this regard, Tehran one of the largest industrial cities of Iran during 2016 and 2017 was selected as the study area.   To assess ground level PM2.5 and PM10, a number of data sets from various sources were collected for this research, including 2 years daily PM2.5 and PM10 mass concentration at 13 air quality aground stations in Tehran which had sufficient records. Also we used 3 meteorological stations including Geophysic, Mehrabad, and Shemiran. Groundbased meteorological parameters (a total of five parameters), including surface wind speed (SPD), surface temperature (ST), visibility (Vis), and surface relative humidity (SRH), and dew point temperature (Td) were obtained from the meteorological stations.  MISR instrument onboard the EOSTerra satellite measures the reflected and scattered sunlight from the Earth in four spectral bands of 446, 558, 672, and 866 nm at each of the nine viewing angles (nadir, ±26.1, ±45.6, ±60.0, and ±70.5°). It has a much narrower swath of about 380 km compared with that of about 2330 km of MODIS. The level 2 MISR AOD has the spatial resolution of 4.4 km and the temporal coverage is about once per week in mid latitude. The latest global MISR AOD product (version 22) for 2013 can be downloaded from the Atmospheric Sciences Data Center at NASA/Langley Research Center (http:// eosweb.larc.nasa.gov). Since the data stations are dispersed, PM monitoring sites were matched with meteorological stations based on the neighboring. On the other hand, as MISR pixels are not distributed equally all over the region, we match them to closest PM monitoring site. Previous studies show that the distance between AOD pixels and closest PM monitoring ground stations must be within 25 km. In order to retrieve the groundlevel PM value proper conversion should be made first. Related researches have shown that the meteorological conditions (such as relative humidity) can strongly impact models for the AOD–PM relationship, as particle extinction properties can change substantially with different vertical mixing and aerosol hygroscopic growth. Thus, |there is a number of methods which proposed to use meteorological factors to improve the relationship between AOD and groundlevel PM. In this study, we developed linear and nonlinear models using MISR AOD values coupled with meteorological parameters for estimating daily groundlevel PM concentration:   where PM is daily groundlevel PM2.5 or PM10 mass concentration (μg/m3), and AOD is MISRderived AOD (unit less). c1 is the intercept Regression coefficients. c2–c7 are associated with predictor variables, including AOD, visibility (km), surface wind speed (m/s), surface temperature (°C), surface relative humidity (%), and dew point(°C). In some studies, an exponential function is used for visibility, relative humidity and dew point temperature. Furthermore, surface meteorological conditions including surface temperature and surface wind speed are employed in the model to further amend the corrected AOD to obtain a better relationship between AOD and PM. Here is the none linear model:   As the third approach, neural network algorithms blackbox models of artificial intelligence were employed. We used the artificial neural network (ANN) algorithm to model groundlevel PM concentration based on the meteorological variables and satellitederived AOD data. We used a backpropagation neural network (BPNN) algorithm to build the groundlevel PM estimation model for predicting the PM concentration. The estimation of the groundlevel PM concentration model consists of six in the input layer, six neurons in the hidden layer, and one neuron in the output layer. The seven parameters in the input layer include, surface temperature, wind speed, relative humidity, visibility, dew point temperature and MISR AOD products. The neuron in the output layer is PM concentration. Cross validation (CV) of all models was conducted by leaving 10 percent of entire PM monitoring site out, fitting the model without this part, and predicting daily PM from 2016 to 2017 for the left out data with the fitted model. This was iterated so that every data was left out one at a time, and the associations between the predicted values and the observed values were assessed to examine model performance.   Linear and nonlinear regression models had similar predictability for groundlevel PM10 (respectively correlation coefficients of 41% and 45%) and PM2.5 (respectively correlation coefficients of 46% and 42%). Artificial neural networks improved the estimation of surface PM2.5 and PM10 significantly and the correlation coefficients improved to 60% and 65%. for PM2.5 and PM10, respectively. It is clear that the neural network model enhanced the regression models result about 19 to 23 percent. The result can use in the regions which there is not sufficient air quality stations.