Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador.

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Selective NO2 Detection of CaCu3Ti4O12 Ceramic Prepared by the Sol-Gel Technique and DRIFT Measurements to Elucidate the Gas Sensing Mechanism.

NO2 is one of the main greenhouse gases, which is mainly generated by the combustion of fossil fuels. In addition to its contribution to global warming, this gas is also directly dangerous to humans. The present work reports the structural and gas sensing properties of the CaCu3Ti4O12 compound prepared by the sol-gel technique. Rietveld refinement confirmed the formation of the pseudo-cubic CaCu3Ti4O12 compound, with less than 4 wt% of the secondary phases. The microstructural and elemental composition analysis were carried out using scanning electron microscopy and X-ray energy dispersive spectroscopy, respectively, while the elemental oxidation states of the samples were determined by X-ray photoelectron spectroscopy. The gas sensing response of the samples was performed for different concentrations of NO2, H2, CO, C2H2 and C2H4 at temperatures between 100 and 300 °C. The materials exhibited selectivity for NO2, showing a greater sensor signal at 250 °C, which was correlated with the highest concentration of nitrite and nitrate species on the CCTO surface using DRIFT spectroscopy.

Predicting fine-scale daily NO2 over Mexico City using an ensemble modeling approach.

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO2) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO2 column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO2 to ground-level NO2 using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated rootmean-squared error (RMSE) of the GAM was 3.95 µg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO2 estimates for further epidemiologic studies in Mexico City.

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak.

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO2 column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO2 and particulate material (PM; coarse: PM10 and fine: PM2.5)]. For this purpose, tropospheric NO2 obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM10, most stations showed correlations lower than 0.2, which were not significant. The results for PM2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO2 proved to be a good predictor for NO2 concentrations at ground level. Considering all stations with NO2 measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO2 throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO2) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO2). Our results demonstrate that Tropospheric NO2 column densities can serve as good predictors of NO2 concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

Impact of mobility restrictions on NO2 concentrations in key Latin American cities during the first wave of the COVID-19 pandemic.

Between March and June 2020, activity in the major cities of Latin America declined due to containment efforts implemented by local governments to avoid the rapid spread of COVID-19. Our study compared 2020 with the previous year and demonstrated a considerable drop in tropospheric NO2 levels obtained by the SENTINEL 5P satellite in major Latin American cities. Lima (47.5%), Santiago (36.1%), São Paulo (27%), Rio de Janeiro (23%), Quito (18.6%), Bogota (17.5%), Buenos Aires (16.6%), Guayaquil (15.3%), Medellin (14.2%), La Paz (9.5%), Belo Horizonte (7.8%), Mexico (7.6%) and Brasilia (5.9%) registered statistically significant decreases in NO2 concentrations during the study period. In addition, we analyzed mobility data from Google and Apple reports as well as meteorological information from atmospheric reanalysis data along with satellite fields between 2011 and 2020, and performed a refined multivariate analysis (non-negative matrix approximation) to show that this decrease was associated with a reduction in population mobility rather than meteorological factors. Our findings corroborate the argument that confinement scenarios may indicate how air pollutant concentrations can be effectively reduced and managed.

Global prediction model for COVID-19 pandemic with the characteristics of the multiple peaks and local fluctuations.

BACKGROUND: With the spread of COVID-19, the time-series prediction of COVID-19 has become a research hotspot. Unlike previous epidemics, COVID-19 has a new pattern of long-time series, large fluctuations, and multiple peaks. Traditional dynamical models are limited to curves with short-time series, single peak, smoothness, and symmetry. Secondly, most of these models have unknown parameters, which bring greater ambiguity and uncertainty. There are still major shortcomings in the integration of multiple factors, such as human interventions, environmental factors, and transmission mechanisms. METHODS: A dynamical model with only infected humans and removed humans was established. Then the process of COVID-19 spread was segmented using a local smoother. The change of infection rate at different stages was quantified using the continuous and periodic Logistic growth function to quantitatively describe the comprehensive effects of natural and human factors. Then, a non-linear variable and NO2 concentrations were introduced to qualify the number of people who have been prevented from infection through human interventions. RESULTS: The experiments and analysis showed the R2 of fitting for the US, UK, India, Brazil, Russia, and Germany was 0.841, 0.977, 0.974, 0.659, 0.992, and 0.753, respectively. The prediction accuracy of the US, UK, India, Brazil, Russia, and Germany in October was 0.331, 0.127, 0.112, 0.376, 0.043, and 0.445, respectively. CONCLUSION: The model can not only better describe the effects of human interventions but also better simulate the temporal evolution of COVID-19 with local fluctuations and multiple peaks, which can provide valuable assistant decision-making information.

Does air pollution explain COVID-19 fatality and mortality rates? A multi-city study in São Paulo state, Brazil.

Since air pollution compromise the respiratory system and COVID-19 disease is caused by a respiratory virus, it is expected that air pollution plays an important role in the current COVID-19 pandemic. Exploratory studies have observed positive associations between air pollution and COVID-19 cases, deaths, fatality, and mortality rate. However, no study focused on Brazil, one of the most affected countries by the pandemic. Thus, this study aimed to understand how long-term exposure to PM10, PM2.5, and NO2 contributed to COVID-19 fatality and mortality rates in São Paulo state in 2020. Air quality data between 2015 and 2019 in 64 monitoring stations within 36 municipalities were considered. The COVID-19 fatality was calculated considering cases and deaths from the government's official data and the mortality rate was calculated considering the 2020 population. Linear regression models were well-fitted for PM2.5 concentration and fatality (R2 = 0.416; p = 0.003), NO2 concentration and fatality (R2 = 0.232; p = 0.005), and NO2 concentration and mortality (R2 = 0.273; p = 0.002). This study corroborates other authors' findings and enriches the discussion for having considered a longer time series to represent long-term exposure to the pollutants and for having considered one of the regions with the highest incidence of COVID-19 in the world. Thus, it reinforces measures to reduce the concentration of air pollutants which are essential for public health and will increase the chance to survive in future respiratory disease epidemics.

Air pollution exposure and incidence of type 2 diabetes in women: A prospective analysis from the Mexican Teachers' Cohort.

BACKGROUND: Air pollution is a risk factor for type 2 diabetes (T2D). However, scarse longitudinal studies have evaluated this association in low- and middle-income countries, where 80% of the worldwide cases of T2D occur. OBJECTIVE: Our aim was to estimate the association between PM2.5 and NO2 exposure and incident T2D, in the Mexican Teachers' Cohort (MTC). METHODS: We selected a subsample of female teachers from the MTC from Mexico City metropolitan area (MCMA), recruited in 2008 and with active follow-up every three years. We assigned the monthly time-weighted exposures (PM2.5 and NO2) using home and work addresses, until failure, censoring or death. We developed two high resolution (1 × 1-km) spatiotemporal predictive generalized additive models of PM2.5 and NO2. Incident diabetes was identified through self-report and two administrative databases of registered diabetes patients. We fitted time-varying Cox models to estimate hazard ratios of the relation between PM2.5 and NO2 and incident T2D, adjusting for confounding variables that were identified using a causal model. RESULTS: A total of 13,669 teachers were followed-up for a maximum of 11.5 years, over which 996 incident T2D cases (88 cases per 100,000 person-months) occurred. Incident T2D increased by 72% (HR = 1.72 [1.47-2.01]) for each 10 µg/m3 increase of PM2.5, and 52% for each 10 ppb of NO2 (HR = 1.52 [1.37-1.68]). DISCUSSION: Mid-term exposure to PM2.5 and NO2 was associated with a higher risk of T2D after adjusting for indoor wood smoke, socioeconomic status, and physical activity. These associations were attenuated in two-pollutant models but remained positive when evaluated long-term exposure. This is the first prospective study to evaluate T2D risk by exposure to both pollutants, PM2.5 and NO2 in a population from an upper middle-income country in the Americas.

Exposure and dose assessment of school children to air pollutants in a tropical coastal-urban area.

This study estimates exposure and inhaled dose to air pollutants of children residing in a tropical coastal-urban area in Southeast Brazil. For that, twenty-one children filled their time-activities diaries and wore the passive samplers to monitor NO2. The personal exposure was also estimated using data provided by the combination of WRF-Urban/GEOS-Chem/CMAQ models, and the nearby monitoring station. Indoor/outdoor ratios were used to consider the amount of time spent indoors by children in homes and schools. The model's performance was assessed by comparing the modelled data with concentrations measured by urban monitoring stations. A sensitivity analyses was also performed to evaluate the impact of the model's height on the air pollutant concentrations. The results showed that the mean children's personal exposure to NO2 predicted by the model (22.3 µg/m3) was nearly twice to those measured by the passive samplers (12.3 µg/m3). In contrast, the nearest urban monitoring station did not represent the personal exposure to NO2 (9.3 µg/m3), suggesting a bias in the quantification of previous epidemiological studies. The building effect parameterisation (BEP) together with the lowering of the model height enhanced the air pollutant concentrations and the exposure of children to air pollutants. With the use of the CMAQ model, exposure to O3, PM10, PM2.5, and PM1 was also estimated and revealed that the daily children's personal exposure was 13.4, 38.9, 32.9, and 9.6 µg/m3, respectively. Meanwhile, the potential inhalation daily dose was 570-667 µg for PM2.5, 684-789 µg for PM10, and 163-194 µg for PM1, showing to be favourable to cause adverse health effects. The exposure of children to air pollutants estimated by the numerical model in this work was comparable to other studies found in the literature, showing one of the advantages of using the modelling approach since some air pollutants are poorly spatially represented and/or are not routinely monitored by environmental agencies in many regions.

The association between air pollution and COVID-19 related mortality in Santiago, Chile: A daily time series analysis.

BACKGROUND: Exposure to ambient air pollution is a risk factor for morbidity and mortality from lung and heart disease. RESEARCH QUESTION: Does short term exposure to ambient air pollution influence COVID-19 related mortality? STUDY DESIGN AND METHODOLOGY: Using time series analyses we tested the association between daily changes in air pollution measured by stationary monitors in and around Santiago, Chile and deaths from laboratory confirmed or suspected COVID-19 between March 16 and August 31, 2020. Results were adjusted for temporal trends, temperature and humidity, and stratified by age and sex. RESULTS: There were 10,069 COVID-19 related deaths of which 7659 were laboratory confirmed. Using distributed lags, the cumulative relative risk (RR) (95% CI) of mortality for an interquartile range (IQR) increase in CO, NO2 and PM2.5 were 1.061 (1.033-1.089), 1.067 (1.023-1.103) and 1.058 (1.034-1.082), respectively There were no significant differences in RR by sex.. In those at least 85 years old, an IQR increase in NO2 was associated with a 12.7% (95% CI 4.2-22.2) increase in daily mortality. CONCLUSION: This study provides evidence that daily increases in air pollution increase the risk of dying from COVID-19, especially in the elderly.

Air pollution and indoor settings.

Indoor environments contribute significantly to total human exposure to air pollutants, as people spend most of their time indoors. Household air pollution (HAP) resulting from cooking with polluting ("dirty") fuels, which include coal, kerosene, and biomass (wood, charcoal, crop residues, and animal manure) is a global environmental health problem. Indoor pollutants are gases, particulates, toxins, and microorganisms among others, that can have an impact especially on the health of children and adults through a combination of different mechanisms on oxidative stress and gene activation, epigenetic, cellular, and immunological systems. Air pollution is a major risk factor and contributor to morbidity and mortality from major chronic diseases. Children are significantly affected by the impact of the environment due to biological immaturity, prenatal and postnatal lung development. Poor air quality has been related to an increased prevalence of clinical manifestations of allergic asthma and rhinitis. Health professionals should increase their role in managing the exposure of children and adults to air pollution with better methods of care, prevention, and collective action. Interventions to reduce household pollutants may promote health and can be achieved with education, community, and health professional involvement.

São Paulo's atmospheric pollution reduction and its social isolation effect, Brazil.

Since January 2020, studies report reductions in air pollution among several countries due to social isolation measures, which have been adopted in order to contain the coronavirus outbreak progress (COVID-19). This study aims to evaluate the change in the atmospheric pollution levels by NO and NO2 in São Paulo City for the social isolation period. The NO and NO2 hourly concentrations were obtained through air quality monitoring stations from CETESB, from January 14, 2020 to April 12, 2020. Mann-Kendall and the Pettitt tests were performed in the air pollutant time series. We observed an overall negative trend in all stations, indicating a decreasing temporal pattern in concentrations. Regarding NO, the highest absolute decrease rates were observed in the Congonhas (- 6.39 µg m-3 month-1) and Marginal Tietê (- 6.19 µg m-3 month-1) stations; regarding NO2, the highest rates were observed in the Marginal Tietê (- 4.45 µg m-3 month-1) and Cerqueira César (- 4.34 µg m-3 month-1) stations. In addition, we identified a turning point in the NO and NO2 series trends that occurred close to the start date of the social isolation period (March 20, 2020). Moreover, from statistical analysis, it was found that NO2 is a suitable surrogate for monitoring economic activities during social isolation periods. Thus, we concluded that social isolation measures implemented on March 20, 2020 caused significant changes in the air pollutant concentrations in the city of São Paulo (as high as - 200% in NO2 levels).

Impacts of the COVID-19 lockdown on air quality and its association with human mortality trends in megapolis Mexico City.

Mexico City is the second most populated city in Latin America, and it went through two partial lockdowns between April 1 and May 31, 2020, for reducing the COVID-19 propagation. The present study assessed air quality and its association with human mortality rates during the lockdown by estimating changes observed in air pollutants (CO, NO2, O3, SO2, PM10 and PM2.5) between the lockdown (April 1-May 31) and prelockdown (January 1-March 31) periods, as well as by comparing the air quality data of lockdown period with the same interval of previous 5 years (2015-2019). Concentrations of NO2 (- 29%), SO2 (- 55%) and PM10 (- 11%) declined and the contents of CO (+ 1.1%), PM2.5 (+ 19%) and O3 (+ 63%) increased during the lockdown compared to the prelockdown period. This study also estimated that NO2, SO2, CO, PM10 and PM2.5 reduced by 19-36%, and O3 enhanced by 14% compared to the average of 2015-2019. Reduction in traffic as well as less emission from vehicle exhausts led to remarkable decline in NO2, SO2 and PM10. The significant positive associations of PM2.5, CO and O3 with the numbers of COVID-19 infections and deaths, however, underscored the necessity to enforce air pollution regulations to protect human health in one of the important cities of the northern hemisphere. Graphical abstract.

Twelve-Year Analysis of NO2 Concentration Measurements at Belisario Station (Quito, Ecuador) Using Statistical Inference Techniques.

In this paper, a robust analysis of nitrogen dioxide (NO2) concentration measurements taken at Belisario station (Quito, Ecuador) was performed. The data used for the analysis constitute a set of measurements taken from 1 January 2008 to 31 December 2019. Furthermore, the analysis was carried out in a robust way, defining variables that represent years, months, days and hours, and classifying these variables based on estimates of the central tendency and dispersion of the data. The estimators used here were classic, nonparametric, based on a bootstrap method, and robust. Additionally, confidence intervals based on these estimators were built, and these intervals were used to categorize the variables under study. The results of this research showed that the NO2 concentration at Belisario station is not harmful to humans. Moreover, it was shown that this concentration tends to be stable across the years, changes slightly during the days of the week, and varies greatly when analyzed by months and hours of the day. Here, the precision provided by both nonparametric and robust statistical methods served to comprehensively proof the aforementioned. Finally, it can be concluded that the city of Quito is progressing on the right path in terms of improving air quality, because it has been shown that there is a decreasing tendency in the NO2 concentration across the years. In addition, according to the Quito Air Quality Index, most of the observations are in either the desirable level or acceptable level of air pollution, and the number of observations that are in the desirable level of air pollution increases across the years.

Has air quality improved in Ecuador during the COVID-19 pandemic? A parametric analysis.

Many governments around the world have enforced quarantine policies to control the spread of the new coronavirus (SARS-CoV-2). These policies have had positive and negative effects on the environment. For example, the concentrations of certain harmful pollutants have decreased in some countries. In contrast, the concentrations of other pollutants have increased. This research analyzes the effect of quarantine policies on air quality in Quito, Ecuador. Using a parametric approach, it was found that NO2 and PM2.5 concentrations have decreased significantly since the establishment of lockdown measures. However, O3 concentrations have increased considerably in 2020.

Air quality variations in Northern South America during the COVID-19 lockdown.

Lockdown measures led to air pollution decrease in several countries around the world such as China and India, whereas other regions experimented an increase in pollutant concentrations. Northern South America (NSA) was one of those areas where pollution changed during lockdown due to high fire activity. This study aims to analyze, for the first time in NSA, the behavior of selected criteria air pollutants during the implementation of the SARS-CoV-2 lockdown in two high populated cities of the region: Bogotá and Medellín in Colombia. A set of tools including surface measurements, as well as satellite and modeled data were used. 24-hour average concentrations of PM10, PM2.5, and NO2 were collected from air quality stations for the lockdown period ranging from February 21 to June 30, 2020. The Copernicus Atmosphere Monitoring Service (CAMS) was used to analyze the fire flux OC as a biomass burning (BB) indicator, and tropospheric NO2 concentrations were retrieved from TROPOMI. The HYSPLIT model was used to analyze back trajectories and fire data were obtained from MODIS sensor measurements. Our analysis shows short-term background NO2, PM10, and PM2.5 concentration reductions of 60%, 44%, and 40%, respectively, for the strict lockdown; and 62%, 58%, and 69% for the relaxed lockdown. Corresponding long-term reductions were of 50%, 32%, and 9% for the strict lockdown; and 37%, 29%, and 19% for the relaxed lockdown. Regional BB increased PM2.5 concentrations by 20 µg/m3 during the strict lockdown, and the Saharan dust event increased PM10 concentrations up to 168 µg/m3 in Bogotá, and 104 µg/m3 in Medellín, bringing an additional risk of morbidity and mortality for population. Regional BB has several causes that need to be properly managed to benefit local air quality improvement plans. Future cleaner transport policies equivalent to reduced lockdown mobility could bring pollution close to WHO guidelines.

COVID-19 pandemic: Impacts on the air quality during the partial lockdown in São Paulo state, Brazil.

In early March 2020, the World Health Organization declared the COVID-19 as a pandemic, and in late March 2020 partial lockdown was ordered by the São Paulo state government. The aim of this study was to assess impacts on air quality in São Paulo - Brazil, during the partial lockdown implemented to provide social distancing required due to the COVID-19 pandemic. We have analyzed data from four air quality stations in São Paulo, Brazil to assess air pollutant concentration variations during the partial lockdown. Data were compared to the five-year monthly mean and to the four-week before the partial lockdown. Overall, drastic reductions on NO (up to -77.3%), NO2 (up to -54.3%), and CO (up to -64.8%) concentrations were observed in the urban area during partial lockdown compared to the five-year monthly mean. By contrast, an increase of approximately 30% in ozone concentrations was observed in urban areas highly influenced by vehicle traffic, probably related to nitrogen monoxide decreases. Although the partial lockdown has contributed to a positive impact on air quality, it is important to take into account the negative impacts on social aspects, considering the deaths caused by COVID-19 and also the dramatic economic effects.

Fluorescence emission spectra of target chloroplast metabolites (flavonoids, carotenoids, lipofuscins, pheophytins) as biomarkers of air pollutants and seasonal tropical climate.

Chloroplasts have luminescent metabolites-chlorophyll being the most known one-whose fluorescence emission may be a useful tool to assess the physiological status of the plant. Some antioxidants (flavonoids and carotenoids), and byproducts of membrane rupture (lipofuscins) and chlorophyll degradation (pheophytins), are chloroplasts' fluorescent metabolites directly involved in plant response to environmental stressors and pollutants and may act as a biomarker of stress. Here we hypothesized that climatic variations and air pollutants induce alterations in the emission profile of chloroplasts' fluorescent metabolites in Tillandsia usneoides (Bromeliaceae). To test this hypothesis, an active biomonitoring study was performed during 2 years in five polluted sites located at the Metropolitan Region of Campinas (São Paulo State, Brazil), aiming to identify target chloroplasts' fluorescent metabolites acting as biomarkers of environmental stress. In situ identification and quantification of the intensity of the fluorescence emission from target metabolites (flavonoids, carotenoids, lipofuscins, and pheophytins) were performed by the observation of fresh leaf sections under confocal laser scanning microscopy. Changes in the profile of fluorescence emission were correlated with local climate and air pollution data. The fluorescence emissions of flavonoids and carotenoids varied seasonally, with significant influence of rainfall and NO2. Our results expand the use of T. usneoides as a bioindicator by using alterations in the fluorescence emission profile of chloroplast metabolites. This application may be especially interesting for NO2 biomonitoring.

Measurement of Nitric Oxide (NO) Generation Rate by Chloroplasts Employing Electron Spin Resonance (ESR).

Chloroplasts are among the more active organelles involved in free energy transduction in plants (photophosphorylation). Nitric oxide (NO) generation by soybean (Glycine max, var ADM 4800) chloroplasts was measured as an endogenous product assessed by electron paramagnetic resonance (ESR) spin-trapping technique. ESR spectroscopy is a methodology employed to detect species with unpaired electrons (paramagnetic). This technology has been successfully applied to different plant tissues and subcellular compartments to asses both, NO content and generation. The spin trap MGD-Fe(2+) is extensively employed to efficiently detect NO. Here, we describe a simple methodology to asses NO generation rate by isolated chloroplasts in the presence of either L-Arginine or nitrite (NO2 (-)) as substrates, since these compounds are required for enzymatic activities considered as the possible sources of NO generation in plants.

Inhibition of Mycobacterium tuberculosis PknG by non-catalytic rubredoxin domain specific modification: reaction of an electrophilic nitro-fatty acid with the Fe-S center.

PknG from Mycobacterium tuberculosis is a Ser/Thr protein kinase that regulates key metabolic processes within the bacterial cell as well as signaling pathways from the infected host cell. This multidomain protein has a conserved canonical kinase domain with N- and C-terminal flanking regions of unclear functional roles. The N-terminus harbors a rubredoxin-like domain (Rbx), a bacterial protein module characterized by an iron ion coordinated by four cysteine residues. Disruption of the Rbx-metal binding site by simultaneous mutations of all the key cysteine residues significantly impairs PknG activity. This encouraged us to evaluate the effect of a nitro-fatty acid (9- and 10-nitro-octadeca-9-cis-enoic acid; OA-NO2) on PknG activity. Fatty acid nitroalkenes are electrophilic species produced during inflammation and metabolism that react with nucleophilic residues of target proteins (i.e., Cys and His), modulating protein function and subcellular distribution in a reversible manner. Here, we show that OA-NO2 inhibits kinase activity by covalently adducting PknG remote from the catalytic domain. Mass spectrometry-based analysis established that cysteines located at Rbx are the specific targets of the nitroalkene. Cys-nitroalkylation is a Michael addition reaction typically reverted by thiols. However, the reversible OA-NO2-mediated nitroalkylation of the kinase results in an irreversible inhibition of PknG. Cys adduction by OA-NO2 induced iron release from the Rbx domain, revealing a new strategy for the specific inhibition of PknG. These results affirm the relevance of the Rbx domain as a target for PknG inhibition and support that electrophilic lipid reactions of Rbx-Cys may represent a new drug strategy for specific PknG inhibition.