Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran

Using deep ensemble forest for high-resolution mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran

High-resolution mapping of PM2.5 concentration over Tehran city is challenging because of the complicated behavior of numerous sources of pollution and the insufficient number of ground air quality monitoring stations. Alternatively, high-resolution satellite Aerosol Optical Depth (AOD) data can be employed for high-resolution mapping of PM2.5. For this purpose, different data-driven methods have been used in the literature. Recently, deep learning methods have demonstrated their ability to estimate PM2.5 from AOD data. However, these methods have several weaknesses in solving the problem of estimating PM2.5 from satellite AOD data. In this paper, the potential of the deep ensemble forest method for estimating the PM2.5 concentration from AOD data was evaluated. The results showed that the deep ensemble forest method with [Formula: see text] gives a higher accuracy of PM2.5 estimation than deep learning methods ([Formula: see text]) as well as classic data-driven methods such as random forest ([Formula: see text]). Additionally, the estimated values of PM2.5 using the deep ensemble forest algorithm were used along with ground data to generate a high-resolution map of PM2.5. Evaluation of produced PM2.5 map revealed the good performance of the deep ensemble forest for modeling the variation of PM2.5 in the city of Tehran.

Tropospheric Ozone in Tehran, Iran, during the last 20 years

Air pollution and its effects on human health and the environment are one of the main concerns in urban areas. This study focuses on the distribution and changes in the concentrations of ozone and its precursors (i.e., NO, NO₂ and CO) in Tehran for the 20-year period from 2001 to 2020. The effects of precursors and meteorological conditions (temperature, wind speed, dew point, humidity and rainfall) on ozone were investigated using data from 22 stations of the Air Quality Control Company (AQCC) and meteorological stations. Regression models were applied to evaluate the dependence of ozone concentration on its precursors and meteorological parameters based on monthly average values. Finally, the monthly and annual levels of surface ozone and total column ozone were compared during the study period. The results show that the average ozone concentration in Tehran varied substantially between 2001 and 2008, and decreased after 2008 when stringent air quality control measures were implemented. The highest average concentration of ozone occurred in the southwest of Tehran. Although mobile and resident sources play an important role in the release of precursors, the results also indicate a significant effect of meteorological conditions on the changes in ozone concentration. This study is an effective step toward a better understanding of ozone changes in Tehran under the changing influence of precursors and meteorological conditions.

A Review of Natural Products for Prevention of Acute Kidney Injury

BACKGROUND AND OBJECTIVES

acute kidney injury (AKI), formerly called acute renal failure (ARF), is commonly defined as an abrupt decline in renal function, clinically manifesting as a reversible acute increase in nitrogen waste products-measured by blood urea nitrogen (BUN) and serum creatinine levels-over the course of hours to weeks. AKI occurs in about 20% of all hospitalized patients and is more common in the elderly. Therefore, it is necessary to prevent the occurrence of AKI, and to detect and treat early, since it is known that a prolonged period of kidney injury increases cardiovascular complications and the risk of death. Despite advances in modern medicine, there are no consistent treatment strategies for preventing the progression to chronic kidney disease. Through many studies, the safety and efficacy of natural products have been proven, and based on this, the time and cost required for new drug development can be reduced. In addition, research results on natural products are highly anticipated in the prevention and treatment of various diseases. In relation to AKI, many papers have reported that many natural products can prevent and treat AKI.

CONCLUSIONS

in this paper, the results of studies on natural products related to AKI were found and summarized, and the mechanism by which the efficacy of AKI was demonstrated was reviewed. Many natural products show that AKI can be prevented and treated, suggesting that these natural products can help to develop new drugs. In addition, we may be helpful to elucidate additional mechanisms and meta-analysis in future natural product studies.

Using infrared geostationary remote sensing to determine particulate matter ground-level composition and concentration

Speciated ground-level aerosol concentrations are required to understand and mitigate health impacts from dust storms, wildfires, and other aerosol emissions. Globally, surface monitoring is limited due to cost and infrastructure demands. While remote sensing can help estimate respirable (i.e. ground level) concentrations, current observations are restricted by inadequate spatiotemporal resolution, uncertainty in aerosol type, particle size, and vertical profile. One key issue with current remote sensing datasets is that they are derived from reflectances observed by polar-orbiting imagers, which means that aerosol is only derived during the daytime and only once or twice per day. Newer quantification methods using geostationary infrared (IR) data have focussed on detecting the presence, or absence, of an event. The determination of aerosol composition or particle size using IR exclusively has received little attention. This manuscript summarizes four scientific papers, published as part of a larger study, and identifies requirements for (a) using infrared radiance observations to obtain continual (i.e. day and night) concentration estimates; (b) increasing temporal resolution by using geostationary satellites; (c) utilizing all infrared channels to maximize spectral differences due to compositional changes; and (d) applying a high-pass filter (brightness temperature differences) to identify compositional variability. Additionally, (e) a preliminary calibration methodology was tested against three severe air quality case study incidents, namely, a dust storm, smoke from prescribed burns, and an ozone smog incident, near Sydney in eastern Australia which highlighted the ability of the method to determine atmospheric stability, clouds, and particle size. Geostationary remote sensing provides near-continuous data at a temporal resolution comparable to monitoring equipment. The spatial resolution (~ 4 km2 at NADIR) is adequate for large sources but coarse for localized sources. The spectral sensitivity of aerosol is limited and appears to be dominated by humidity changes rather than concentration or compositional changes. Geostationary remote sensing can be used to determine the timing, duration, and spatial extent of an air quality event. Brightness temperature differences can assist in qualifying composition with an order of magnitude estimate of concentration.

Estimating ground-level PM2.5 concentrations by developing and optimizing machine learning and statistical models using 3 km MODIS AODs: case study of Tehran, Iran

PURPOSE

In this study we aimed to develop an optimized prediction model to estimate a fine-resolution grid of ground-level PM_2.5 levels over Tehran. Using remote sensing data to obtain fine-resolution grids of particulate levels in highly polluted environments in areas such as Middle East with the abundance of brightly reflecting deserts is challenging.

METHODS

Different prediction models implementing 3 km AOD products from the MODIS collection 6 and various effective parameters were used to obtain a reliable model to estimate ground-level PM_2.5 concentrations. In this regards, the linear mixed effect model (LME), multi-variable linear regression model (MLR), Gaussian process model (GPM), artificial neural network (ANN) and support vector regression (SVR) were developed and their performance were compared. Since the LME and GPM outperformed other models, they were further optimized based on meteorological and topographical variables. These models were used to estimate PM_2.5 values over the highly polluted megacity, Tehran, Iran. Moreover, the influence of site effect term on the performance of different shapes of LME models was evaluated by considering the random intercept for sites.

RESULTS

Results showed LME models without the site effect term were able to explain ground-level variabilities of PM_2.5 concentrations in ranges of 60-66% (RMSE = 9.6 to 10.3 μg/m³) and 35-41% (RMSE = 12.7 to 13.3 μg/m³) during the model-fitting and cross-validation, respectively. By considering the site effect term, the performance of LME models during calibrations and validations improved by 20% and 50% on average, respectively (18.5% and 17% decrease in the RSME) as compared to LME models without the site effect term. The optimized shape of LME models had a good agreement during both model-fitting (R² of 0.76) and cross-validation (R² of 0.6). Site-specific and seasonal performances of all types of models revealed that LME models had highest R² values over all monitoring stations and all seasons during the cross-validation. LME models had the best performance in May and March compared to other months during the model-fitting and cross-validation. However, LME models had a significant weakness in predicting extreme values of PM_2.5 during the cross-validation. Among all other types of models, GPM with the R² value of 0.59 and the RMSE of 10.2 μg/m³ had the best performance during the cross-validation.

CONCLUSIONS

While the best shape of LME and GPM had similar and reliable performances in predicting ground-level PM_2.5 values during the cross-validation, GPM was able to predict extreme values of ground-level PM_2.5 concentrations, which was the weakness of LME models and was an important issue in urban polluted environments. In this respect, GPM could be a good alternative for LME models for high levels of PM_2.5 concentrations. The spatial distribution of estimated PM_2.5 values represented that central parts of Tehran were the most polluted area over the studied region which was consistent with the ground-level recording PM_2.5 data over monitoring stations.

Ambient air particulate matter (PM10) satellite monitoring and respiratory health effects assessment

PURPOSE

Air particulate matter with an aerodynamic diameter of 10 µm or less (PM₁₀) is one of the main causes of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study explored the relationship between PM₁₀ by remote sensing and AECOPD in Chaharmahal-o-Bakhtiari province from 2014 to2018.

METHOD

PM₁₀ concentrations were predicted and validated based on aerosol optical depth (AOD) from 161 images processed by MODIS sensor and ground air quality monitoring station data. Demographic information and spirometric indices of 2038 patients with AECOPD were collected and analyzed from the hospital during the studied periods. SPSS software was used to analyze the relationships between these two categories of information.

RESULTS

There was a significant negative relationship between PM10 and FVC, FVC%, FEV1, FEV1%, FEF25-75, FEV1/FVC, PEF, and FEF25FVC indices (p < 0.05). The results showed that over 2014-2018, the annual mean of PM10 concentrations varied from 35 to 52 µg/m3. The result of the regression model showed that the patient's age, body mass index (BMI), and PM₁₀ concentrations were the most affecting variables on the two important spirometric indices i.e., FVC% and FEV₁%. The PM₁₀ concentrations and number of AECOPD patients had a similar pattern during the studied period. The women group, age group above 74 years, normal BMI, and non-smoking patients showed the most sensitivity to the PM₁₀ concentrations.

CONCLUSIONS

Our findings provide supplementary scientific information on PM₁₀ concentration related to the incidence of AECOPD and as a variable affecting the most important spirometry indicators by providing local decision-makers information needed to set a priority of air pollution control measures as well as health services.

Developing air exchange rate models by evaluating vehicle in-cabin air pollutant exposures in a highway and tunnel setting: case study of Tehran, Iran

The passengers inside vehicles could be exposed to high levels of air pollutants particularly while driving on highly polluted and congested traffic roadways. In order to study such exposure levels and its relation to the cabin ventilation condition, a monitoring campaign was conducted to measure the levels inside the three most common types of vehicles in Tehran, Iran (a highly air polluted megacity). In this regard, carbon monoxide (CO) and particulate matter (PM) were measured for various ventilation settings, window positions, and vehicle speeds while driving on the Resalat Highway and through the Resalat Tunnel. Results showed on average in-cabin exposure to particle number and PM₁₀ for the open windows condition was seven times greater when compared to closed windows and air conditioning on. When the vehicle was passing through the tunnel, in-cabin CO and particle number increased 100 and 30%, respectively, compared to driving on highway. Air exchange rate (AER) is a significant factor when evaluating in-cabin air pollutants level. AER was measured and simulated by a model developed through a Monte Carlo analysis of uncertainty and considering two main affecting variables, vehicle speed and fan speed. The lowest AER was 7 h^-1 for the closed window and AC on conditions, whereas the highest AER was measured 70 h^-1 for an open window condition and speed of 90 km h^-1. The results of our study can assist policy makers in controlling in-cabin pollutant exposure and in planning effective strategies for the protection of public health.

Applications of Space Technologies to Global Health: Scoping Review

Background

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

Objective

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

Methods

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

Results

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

Conclusions

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Evaluating near highway air pollutant levels and estimating emission factors: Case study of Tehran, Iran

A field sampling campaign was implemented to evaluate the variation in air pollutants levels near a highway in Tehran, Iran (Hemmat highway). The field measurements were used to estimate road link-based emission factors for average vehicle fleet. These factors were compared with results of an in tunnel measurement campaign (in Resalat tunnel). Roadside and in-tunnel measurements of carbon monoxide (CO) and size-fractionated particulate matter (PM) were conducted during the field campaign. The concentration gradient diagrams showed exponential decay, which represented a substantial decay, more than 50-80%, in air pollutants level in a distance between 100 and 150meters (m) of the highway. The changes in particle size distribution by distancing from highway were also captured and evaluated. The results showed particle size distribution shifted to larger size particles by distancing from highway. The empirical emission factors were obtained by using the roadside and in tunnel measurements with a hypothetical box model, floating machine model, CALINE4, CT-EMFAC or COPERT. Average CO emission factors were estimated to be in a range of 4 to 12g/km, and those of PM10 were 0.1 to 0.2g/km, depending on traffic conditions. Variations of these emission factors under real working condition with speeds were determined.