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1.
ABSTRACT

Rapid economic growth, a high degree of urbanization and the proximity of a large number of desert and semidesert landscapes can have a significant impact on the atmosphere of adjacent territories, leading to high levels of atmospheric pollution. Therefore, identifying possible sources of atmospheric pollution is one of the main tasks. In this study, we carried out an analysis of spatial and temporal characteristics of five main atmospheric pollutants (PM2.5, PM10, SO2, NO2, and CO) near potential source of natural aerosols, affecting seven cities (Wuhai, Alashan, Wuzhong, Zhongwei, Wuwei, Jinchang, Zhangye), located in immediate proximity to the South Gobi deserts. The results, obtained for the period from 1 January 2016 to 31 December 2018, demonstrate total concentrations of PM2.5 and PM10 are 38.2 ± 19.5 and 101 ± 80.7 μg/m3 exceeding the same established by the Chinese National Ambient Air Quality Standard (CNAAQS), being 35 and 70 μg/m3, respectively. Based on the data from Moderate Resolution Imaging Spectroradiometer (MODIS) for the whole period, Clean Сontinental (71.49%) and Mixed (22.29%) types of aerosols prevail in the region. In the spring and winter seasons maximum concentrations of pollutants and high values of Aerosol Optical Depth (AOD) in the region atmosphere are observed. PM2.5 and PM10 ratio shows the presence of coarse aerosols in the total content with value 0.43. The highest concentrations of pollutants were in the period of dust storms activity, when PM2.5 and PM10 content exceeded 200 and 1000 µg/m3, and AOD value exceeded 1. UV Aerosol Index (UVAI), Aerosol Absorbing Optical Depth (AAOD), and Single Scattering Albedo (SSA), obtained from Ozone Monitoring Instrument (OMI), demonstrate the high content of dust aerosols in the period of sandstorms. Analysis of backward trajectories shows that dust air masses moved from North to Northwest China, affecting large deserts such as Taklamakan, Gurbantunggut, Badain Jaran, Tengger, and Ulan Buh deserts.  相似文献   

2.
ABSTRACT

Urbanization in China is closely connected with ambient particulate matter 2.5 (PM2.5). However, the potential for altering PM2.5 through the urban landscape characteristics is uncertain. In this study, we analyzed the urban PM2.5 pollution situation for 2014–2016 and investigated the impact of landscape factors on urban PM2.5 in China at the city level. All the prefecture-level cities were stratified by urban population size into small (<500,000), medium (500,000–1,000,000), and large (>1,000,000), and the other second-level administrative cities were assigned as ‘other’ cities. The multivariate regression model including both urban landscape factors and social-economic variables explained 70.0%, 32.8%, 19.2%, and 12.4% of the arithmetic mean PM2.5 concentration (AMC-PM2.5) for the other, small, medium, and large cities, respectively. With regard to the configuration of land cover, agricultural activity is a major contributor of PM2.5 pollution, for which the explanatory power ranged from 7.6% (for the large cities) to 64% (for the other cities). In addition, grassland aggregation also has a limited but negative effect on urban PM2.5 pollution, despite the negligible effect on dry deposition. Overall, these findings likely reflect the interaction between urban air quality and urbanization, and will have implications for air quality control strategies.  相似文献   

3.
利用遥感数据评价燃煤电厂空气质量   总被引：1，自引：0，他引：1  
卫星观测数据可以评价燃煤电厂的空气质量等级。NO2、SO2 和烟尘是燃煤电厂排放的主要污染物,本文利用卫星遥感观测的NO2、SO2和气溶胶光学厚度AOD(Aerosol Optical Depth)开展燃煤电厂空气质量评价。以中国华北地区为实验区,分析对比了3种污染物不同时间分辨率和空间分辨率的污染状况,确定了单因子的5级分级标准,根据燃煤电厂排放污染物的权重不同,提出了评价近地表空气质量状况的模型。本文综合考虑3种污染因子来反映电厂空气质量,有利于提高评价的准确性以及反应信息的全面性。结果表明,该模型能正确反映不同地区电厂的空气质量特点。  相似文献   

4.
为了验证风云三号D星MERSI传感器的气溶胶光学厚度（AOD）数据对地面PM2.5的污染过程预报的效果,本文基于WRF-Chem（Weather Research and Forecasting model coupled with Chemistry）大气化学模式和三维变分同化方法,针对2020-02-10—2020-02-12中国北方地区的一次PM2.5重污染过程,进行了同化和预报试验研究。同化数据来自常规地面站点的PM2.5浓度数据和风云三号D星MERSI传感器的气溶胶光学厚度（AOD）数据。控制试验不同化任何资料,3组同化试验分别为仅同化地面PM2.5,仅同化卫星AOD,以及同时同化PM2.5和卫星AOD两种资料。结果表明,3组同化试验都可以有效提高初始场准确率,以地面PM2.5作为检验标准,仅同化PM2.5、仅同化AOD、同时同化两种资料相对于控制试验,初始场的平均偏差分别降低54.9%、21.9%和49.0%,平均相关系数分别提升51.4%、16.0%和34.0%,平均均方根误差分别降低50.6%、17.2%和42.3%。以卫星AOD作为检验标准,3组同化试验相对于控制试验,初始场的平均偏差分别降低37.6%、78.4%和83%,平均均方根误差分别降低31.6%、62.2%和65.2%。同化后的初始场对预报有显著的改进,改进持续时间达24 h,以地面PM2.5作为检验标准,同时同化两种资料的试验对24 h预报的平均偏差减少19.7%,相关系数提升8.8%,均方根误差减少17.2%;以卫星AOD作为检验标准,24 h预报的平均偏差减少40.1%,相关系数提升25.9%,均方根误差降低34.7%。试验结论为,相对于仅同化地面PM2.5资料,同化风云卫星AOD资料可以提升后期预报效果。  相似文献   

5.
卫星观测不仅能反映区域宏观大气污染状况,也能从城市尺度上监测大气污染物的变化。基于以上优势,本文利用MODIS气溶胶光学厚度(AOD)和OMI对流层NO_2垂直柱浓度数据,比较2015年与2012年—2014年以及2015年3个时期(减排前、减排中、减排后)AOD和NO_2柱浓度的变化,定性分析了阅兵期间华北平原地区污染物减排效果,重点定量评估北京市联控减排措施的效果。研究发现2015年减排中华北平原重污染地区AOD和NO_2柱浓度相比于前3年同期有明显降低。定量分析北京市的减排效果得到:2015年减排中较前3年同期而言,AOD降低59%,NO_2柱浓度降低41%;较2015年减排前而言,AOD降低73%,NO_2柱浓度降低30%,去除气象条件影响后,AOD下降43%,NO_2柱浓度下降21%,说明严格的联控减排措施有效地改善了空气质量,气象条件也起到积极的作用。减排措施结束后,AOD和NO_2柱浓度比减排中分别增加159%和71%。研究结果表明,卫星遥感与地基监测评估效果相当,能反映北京地面污染物排放能力;它既能观测区域尺度大气污染变化,又可评估城市尺度大气污染减排。随着卫星技术水平的提高,期望未来卫星遥感可作为一种独立手段来定量评估区域及城市尺度空气质量减排措施的效果。  相似文献   

6.
Air pollution is a major problem, conscious both for health and surroundings. This is a novel approach for the design & development of a system for the monitoring of different air pollutants especially at remote places where it is difficult to install any conventional air quality monitoring stations as well as for the cities. In this research work, a framework of Functional air quality index which is an indicator of susceptibility to respiratory illness has been built using the Bayesian neural network to provide the random real-time data about a location through wireless communication. The monitoring system is integrated with different types of sensors to measure the level of different air pollutants or air quality parameters such as Suspended particulate matters, (PM2.5), Nitrogen dioxide, Sulphur dioxide, Ozone which are directly associated with airways inflammatory diseases such as Asthma, Bronchitis, COPD. Each location in Map (GPS) can be updated automatically with fAQI to the user through mobile computing and satellite commutation. The user gets information about the neighborhood location with health-related information such as- whether a particular location is sensitive to respiratory diseases such as Bronchitis, asthma, COPD etc. due to suspended allergen/pollutants in the ambient air. This novel approach is designed with its’ own prototype and an application of Inter of Things in Health GIS for the benefit of humanity.  相似文献   

7.
There has been a great deal of research into the short-term effects of air pollution on health with a large number of studies modelling the association between aggregate disease counts and environmental exposures measured at point locations, for example via air pollution monitors. In such cases, the standard approach is to average the observed measurements from the individual monitors and use this in a log-linear health model. Hence such studies are ecological in nature being based on spatially aggregated health and exposure data. Here we investigate the potential for bias in the estimates of the effects on health when estimating the short-term effects of air pollution on health. Such ecological bias may occur if a simple summary measure, such as a daily mean, is not a suitable summary of a spatially variable pollution surface. We assess the performance of commonly used models when confronted with such issues using simulation studies and compare their performance with a model specifically designed to acknowledge the effects of exposure aggregation. In addition to simulation studies, we apply the models to a case study of the short-term effects of particulate matter on respiratory mortality using data from Greater London for the period 2002–2005. We found a significant increased risk of 3% (95% CI 1–5%) associated with the average of the previous three days exposure to particulate matter (per 10 μg m−3 PM10).  相似文献   

8.
ABSTRACT

The physical processes associated with the constituents of the troposphere, such as aerosols have an immediate impact on human health. This study employs a novel method to calibrate Aerosol Optical Depth (AOD) obtained from the MODerate resolution Imaging Spectrometer (MODIS – Terra satellite) for estimating surface PM2.5 concentration. The Combined Deep Blue Deep Target daily product from the MODIS AOD data acquired across the Indian Subcontinent was used as input, and the daily averaged PM2.5pollution level data obtained from 33 monitoring stations spread across the country was used for calibration. Mixed Effect Models (MEM) is a linear model to deal with non-independent data from multiple levels or hierarchy using fixed and random effects of dependent parameters. MEM was applied to the dataset obtained for the period from January to August 2017. The MEM considers a fixed and random component, where the random components model the daily variations of the AOD – PM2.5 relationships, site-specific adjustment parameters, temporal (meteorological) variables such as temperature, and spatial variables such as the percentage of agricultural area, forest cover, barren land and road density with the resolution of 10 km × 10 km. Estimation accuracy was improved from an R2 value of 0.66 from our earlier study (when PM2.5 was modeled against only AOD and site-specific parameters) toR2 value of 0.75 upon the inclusion of spatiotemporal (meteorological) variables with increased % within Expected Error from 18% to 35%, reduced Mean Bias Error from 3.22 to 0.11 and reduced RMSE from 29.11 to 20.09. We also found that spline interpolation performed better than IDW and Kriging inefficiently estimating the PM2.5 concentrations wherever there were missing AOD data. The estimated minimum PM2.5 is 93 ± 25μg/m3 which itself is in the upper limit of the hazardous level while the maximum is estimated as 170 ± 70μg/m3. The study has thus made it possible to determine the daily spatial variations of PM2.5 concentrations across the Indian subcontinent utilizing satellite-based AOD data.  相似文献   

9.
ABSTRACT

The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants’ respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants’ concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NOx during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO2, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O3 and NOx) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while tra?c emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects.  相似文献   

10.
Low and moderate spatial resolution satellite sensors (such as TOMS, AVHRR, SeaWiFS) have already shown their capability in tracking aerosols at a global scale. Sensors with moderate to high spatial resolution (such as MODIS and MERIS) seem also to be appropriate for aerosol retrieval at a regional scale. We investigated in this study the potential of MERIS-ENVISAT data to resolve the horizontal spatial distribution of aerosols over urban areas, such as the Athens metropolitan area, by using the differential textural analysis (DTA) code. The code was applied to a set of geo-corrected images to retrieve and map aerosol optical thickness (AOT) values relative to a reference image assumed to be clean of pollution with a homogeneous atmosphere. The comparison of satellite retrieved AOT against PM10 data measured at ground level showed a high positive correlation particularly for the AOT values calculated using the 5th MERIS’ spectral band (R2=0.83). These first results suggest that the application of the DTA code on cloud free areas of MERIS images can be used to provide AOT related to air quality in this urban region. The accuracy of retrieved AOT mainly depends on the overall quality, the pollution cleanness and the atmospheric homogeneity of the reference image.  相似文献   

11.
We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.  相似文献   

12.
The 17 Sustainable Development Goals (SDGs) aim to end extreme poverty and create a healthy, sustainable world by the year 2030. Goal 7 is of interest to this study as it targets access to clean and affordable energy. However, in this study we show that the energy created in South Africa is not necessary clean. South Africa has numerous coal-fired power station located in the Mpumalanga (MP), Gauteng (GP) and Limpopo (LP) provinces. These power station produce tons of toxic pollutants including sulphur dioxide (SO2), nitrogen dioxide (NO2) and sulphates (SO4). These pollutants are known to have a negative impact on human health, climate and the environment. In this study we use the sequential Mann-Kendall test to investigate the 39 year (1980–2019) trends of SO2, NO2 and SO4 from these source areas. We also report for the first time on the observations of SO2 and NO2 from the Sentinel-5 P sensor over South Africa. Increasing trends of SO2 were observed in the MP, LP and GP regions. The increase was mostly due to the emissions from coal-fired power stations. Moreover, the increase of SO2 over the years could be due to the increasing demand in electricity, aging power stations and the low quality of coal used. Sentinel-5 P observations of SO2 and NO2 over South Africa were observed in the MP, GP and LP regions as a result of coal-fired power stations. Dispersion of SO2 and NO2 over South Africa were observed in the winter months, while confined SO2 and NO2 in the source region were observed in the summer months.  相似文献   

13.
环境与发展是当今世界共同关注的重大问题。目前我国经济正处于高速发展时期,随着经济社会的快速发展和城镇一体化进程的加快,空气污染问题日趋严重,对人体健康造成很大伤害,因此环境保护的重要性日益突出。本文通过建立环境质量空间数据库,利用地理信息软件ArcGIS为基础平台,将环境空气质量监测数据与地理空间位置进行地理匹配,利用地理信息可视化技术,实现环境空气质量监测数据中各主要空气污染物的浓度时间分布的可视化表达,制作环境空气质量专题地图,有助于有效地从海量监测数据中发现有价值的信息,为环境保护部门提供决策参考依据。  相似文献   

14.
Satellite-based atmospheric CO2 observations have provided a great opportunity to improve our understanding of the global carbon cycle. However, thermal infrared (TIR)-based satellite observations, which are useful for the investigation of vertical distribution and the transport of CO2, have not yet been studied as much as the column amount products derived from shortwave infrared data. In this study, TIR-based satellite CO2 products – from Atmospheric Infrared Sounder, Tropospheric Emission Spectrometer (TES), and Thermal And Near infrared Sensor for carbon Observation – and carbon tracker mole fraction data were compared with in situ Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL) data for different locations. The TES CO2 product showed the best agreement with CONTRAIL CO2 data resulting in R2 ~ 0.87 and root-mean-square error ~0.9. The vertical distribution of CO2 derived by TES strongly depends on the geophysical characteristics of an area. Two different climate regions (i.e., southeastern Japan and southeastern Australia) were examined in terms of the vertical distribution and transport of CO2. Results show that while vertical distribution of CO2 around southeastern Japan was mainly controlled by horizontal and vertical winds, horizontal wind might be a major factor to control the CO2 transport around southeastern Australia. In addition, the vertical transport of CO2 also varies by region, which is mainly controlled by anthropogenic CO2, and horizontal and omega winds. This study improves our understanding of vertical distribution and the transport of CO2, both of which vary by region, using TIR-based satellite CO2 observations and meteorological variables.  相似文献   

15.
The principle and method for solving three types of satellite gravity gradient boundary value problems by least-squares are discussed in detail. Also, kernel function expressions of the least-squares solution of three geodetic boundary value problems with the observations {Γ zz },{Γ xz , Γ yz} and {Γ xx -Γ yy ,2 Γxy}are presented. From the results of recovering gravity field using simulated gravity gradient tensor data, we can draw a conclusion that satellite gravity gradient integral formulas derived from least-squares are valid and rigorous for recovering the gravity field.  相似文献   

16.
This study adopts a near real‐time space‐time cube approach to portray a dynamic urban air pollution scenario across space and time. Originating from time geography, space‐time cubes provide an approach to integrate spatial and temporal air pollution information into a 3D space. The base of the cube represents the variation of air pollution in a 2D geographical space while the height represents time. This way, the changes of pollution over time can be described by the different component layers of the cube from the base up. The diurnal ambient ozone (O3) pollution in Houston, Texas is modeled in this study using the space‐time air pollution cube. Two methods, land use regression (LUR) modeling and spatial interpolation, were applied to build the hourly component layers for the air pollution cube. It was found that the LUR modeling performed better than the spatial interpolation in predicting air pollution level. With the availability of real‐time air pollution data, this approach can be extended to produce real‐time air pollution cube is for more accurate air pollution measurement across space and time, which can provide important support to studies in epidemiology, health geography, and environmental regulation.  相似文献   

17.
Biomass burning from vegetation fires is an important source of greenhouse gas emissions. In this study, we quantify biomass burning emissions from grasslands from the highly sensitive Kaziranga National Park, Assam, Northeast India. Most of the fires in the park are ‘controlled burning fires’ set by the park officials for management purposes. We evaluated the short-term impacts of fires and the resulting air pollution through integrating biomass burnt information from satellite remote sensing datasets. IRS-P6 Advanced Wide Field Sensor (AWiFS) data during March and April corresponding to dry season were evaluated to delineate the burnt areas. These burnt area estimates were then integrated with biomass data and emission factors for quantifying the greenhouse gas emissions. Results suggested that of the total study area of 37,822 ha, nearly 3163.282 ha has been burnt during March, 2005. Within one month, the burnt area increased to 7443.92 ha by April, i.e., from 8.36% to 19.68%. In total, biomass burning from the grasslands contributed to 29.65 Tg CO2, 1.19 Tg CO, 0.071 Tg NOx, 0.042 Tg CH4, 0.0625 Tg total non-methane hydrocarbons, 0.152 Tg of particulate matter, and 0.062 Tg of organic carbon and 0.008 Tg of black carbon during April. The importance of ‘fire’ as a management tool for maintaining the wildlife habitat has been highlighted in addition to some of the adverse affects of air pollution resulting from such management practices. The results from this study will be useful to forest officials as well as policy makers to undertake some sustainable forest management practices to maintain an ideal habitat for Kaziranga's wildlife.  相似文献   

18.
ABSTRACT

The capacity of six water stress factors (ε′i) to track daily light use efficiency (ε) of water-limited ecosystems was evaluated. These factors are computed with remote sensing operational products and a limited amount of ground data: ε′1 uses ground precipitation and air temperature, and satellite incoming global solar radiation; ε′2 uses ground air temperature, and satellite actual evapotranspiration and incoming global solar radiation; ε′3 uses satellite actual and potential evapotranspiration; ε′4 uses satellite soil moisture; ε′5 uses satellite-derived photochemical reflectance index; and ε′6 uses ground vapor pressure deficit. These factors were implemented in a production efficiency model based on Monteith’s approach in order to assess their performance for modeling gross primary production (GPP). Estimated GPP was compared to reference GPP from eddy covariance (EC) measurements (GPPEC) in three sites placed in the Iberian Peninsula (two open shrublands and one savanna). ε′i were correlated to ε, which was calculated by dividing GPPEC by ground measured photosynthetically active radiation (PAR) and satellite-derived fraction of absorbed PAR. Best results were achieved by ε′1, ε′2, ε′3 and ε′4 explaining around 40% and 50% of ε variance in open shurblands and savanna, respectively. In terms of GPP, R2?≈?0.70 were obtained in these cases.  相似文献   

19.
吴浩  王先华  叶函函  蒋芸  段锋华  吕松 《遥感学报》2019,23(6):1223-1231
大气温室气体监测仪GMI（Greenhouse gases Monitor Instrument）是高分五号（GF-5）卫星载荷之一，主要用于全球温室气体含量监测和碳循环研究。高精度反演是卫星大气CO2遥感的基本需求。地表反射率影响卫星遥感辐射量及辐射传输过程中的地气耦合过程，严重制约着CO2的反演精度，针对GMI开发高精度的大气CO2反演算法，地表反射是一个需要重点考虑的因素。城市是CO2重要的发射源，且城市下垫面存在明显的二向反射特性，加上城市大气条件不良，复杂的地气耦合效应存在这都考验反演算法的准确性和鲁棒性。本文针对北京城市地区，利用2011年—2016年共5年的MODIS（MODerate-resolution Imaging Spectroradiometer）地表二向反射分布函数BRDF（Bidirectional Reflectance Distribution Function）数据，构建了适合利用单次观测数据反演的BRDF模型，并提出一种同时反演地表BRDF参数和大气CO2含量的算法。结果表明在550 nm波长处气溶胶光学厚度AOD（Aerosol Optical Depth）小于0.4时，大部分GMI模拟数据的反演误差控制在0.5%（~2 ppm）内。利用GOSAT （Greenhouse gases Observing SATellite）实测数据的反演结果与修正后的日本国立环境研究所NIES（National Institute for Environmental Studies）反演结果进行对比，其平均误差为1.25 ppm，相关性达到0.85。本算法满足GMI数据在北京城市区域高精度CO2反演的需求，并使得反演高值气溶胶区域数据成为可能，增加了GMI观测数据的利用率。  相似文献   

20.
The Earth Observation (EO) data with their advantages in spectral, spatial and temporal resolutions have demonstrated their great value in providing information about many of the components that comprise environmental systems and ecosystems for decades that are crucial to the understating of public health issues. This literature review shows that in conjunction with in situ data collection, EO data have been used to observe, monitor, measure and model many environmental variables that are associated with disease vectors. Furthermore, satellite derived aerosol optical depth has been increasingly employed to estimate ground-level PM2.5 concentrations, which have been found to associate with various health outcomes such as cardiovascular and respiratory diseases. It is suggested that Landsat-like imagery data may provide important data sources to analyse and understand contagious and infectious diseases at the local and regional scales, which are tied to urbanisation and associated impacts on the environment. There is also a great need of data products from coarse resolution imagery, such as those from moderate resolution imaging spectrometer, multiangle imaging spectroradiometer and geostationary operational environmental satellite , to model and characterise infectious diseases at the continental and global scales. The infectious diseases at greater geographical scales have become unprecedentedly significant as global climate change and the process of globalisation intensify. The relationship between infectious diseases and environmental characteristic have been explored by using statistical, geostatistical and physical models, with recent emphasis on the use of machine-learning techniques such as artificial neural networks. Lastly, we suggest that the planned HyspIRI mission is crucial for observing, measuring and modelling environmental variables impacting various diseases as it will improve both spectral resolution and revisit time, thus contributing to better prediction of occurrence of infectious diseases, target intervention and tracking of epidemic events.  相似文献   

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