Airborne environmentally persistent free radicals (EPFRs) in PM2.5 from combustion sources: Abundance, cytotoxicity and potential exposure risks.

As an emerging atmospheric pollutant, airborne environmentally persistent free radicals (EPFRs) are formed during many combustion processes and pose various adverse health effects. In health-oriented air pollution control, it is vital to evaluate the health effects of atmospheric fine particulate matter (PM2.5) from different emission sources. In this study, various types of combustion-derived PM2.5 were collected on filters in a partial-flow dilution tunnel sampling system from three typical emission sources: coal combustion, biomass burning, and automobile exhaust. Substantial concentrations of EPFRs were determined in PM2.5 samples and associated with significant potential exposure risks. Results from in vitro cytotoxicity and oxidative potential assays suggest that EPFRs may cause substantial generation of reactive oxygen species (ROS) upon inhalation exposure to PM2.5 from anthropogenic combustion sources, especially from automobile exhaust. This study provides important evidence for the source- and concentration-dependent health effects of EPFRs in PM2.5 and motivates further assessments to advance public health-oriented PM2.5 emission control.

Associations between short-term ambient ozone exposure and cause-specific mortality in rural and urban areas of Jiangsu, China.

Most previous studies on the acute health effects of ozone are limited to urban areas, largely due to the paucity of air pollutant measurements in rural areas. We here estimated the county-specific daily maximum 8-h average ozone concentration in Jiangsu Province, China during 2015-2018, using a recently developed spatiotemporal machine learning model at a spatial resolution of 0.1° × 0.1° (∼11 × 11 km). Counties were equally divided into urban and rural groups based on the median of the percentage of urban residents across Jiangsu counties obtained from the National Population Census in 2010. We first conducted time-series analyses to estimate the county-specific effect of ozone using generalized linear models, then pooled the effect estimates by random-effects modeling. A 10 µg/m3 increase in the 4-day moving average (lag 0-3) of ambient ozone exposure was associated with increases of 0.66% (95% confidence interval [CI] 0.36%-0.95%) in daily nonaccidental mortality in rural areas and 0.42% in urban areas (95% CI, 0.27%-0.56%). Short-term ambient ozone exposure was associated with an increased risk of mortality caused by chronic obstructive pulmonary disease, hypertension, ischemic heart disease, and stroke. Our finding suggests that both urban and rural residents suffer adverse health effects from short-term ozone exposure.

In vitro assessments of bioaccessibility and bioavailability of PM2.5 trace metals in respiratory and digestive systems and their oxidative potential.

Air pollution is a serious environmental issue. As a key aerosol component, PM2.5 associated toxic trace metals pose significant health risks by inhalation and ingestion, but the evidences and mechanisms were insufficient and not well understood just by their total environmental concentrations. To accurately assess the potential risks of airborne metals, a series of in vitro physiologically based tests with synthetic human lung and gastrointestinal fluids were conducted to assess both the bioaccessibility and bioavailability of various PM2.5 bound metals in the respiratory and digestive systems from both urban and industrial areas of Nanjing city. Moreover, the chemical acellular toxicity test [dithiothreitol (DTT) assay] and source analysis were performed. Generally, the bioaccessibility and bioavailability of investigated metals were element and body fluid dependent. Source oriented metals in PM2.5 showed diverse bioaccessibility in different human organs. The PM2.5 induced oxidative potential was mainly contributed by the bioaccessible/bioavailable transition metals such as Fe, Ni and Co from metallurgic dust and traffic emission. Future researches on the toxicological mechanisms of airborne metals incorporating the bioaccessibility, bioavailability and toxicity tests are directions.

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis.

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears.

Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery.

Powdery mildew is one of the dominant diseases in winter wheat. The accurate monitoring of powdery mildew is important for crop management and production. Satellite-based remote sensing monitoring has been proven as an efficient tool for regional disease detection and monitoring. However, the information provided by single-date satellite scene is hard to achieve acceptable accuracy for powdery mildew disease, and incorporation of early period contextual information of winter wheat can improve this situation. In this study, a multi-temporal satellite data based powdery mildew detecting approach had been developed for regional disease mapping. Firstly, the Lansat-8 scenes that covered six winter wheat growth periods (expressed in chronological order as periods 1 to 6) were collected to calculate typical vegetation indices (VIs), which include disease water stress index (DSWI), optimized soil adjusted vegetation index (OSAVI), shortwave infrared water stress index (SIWSI), and triangular vegetation index (TVI). A multi-temporal VIs-based k-nearest neighbors (KNN) approach was then developed to produce the regional disease distribution. Meanwhile, a backward stepwise elimination method was used to confirm the optimal multi-temporal combination for KNN monitoring model. A classification and regression tree (CART) and back propagation neural networks (BPNN) approaches were used for comparison and validation of initial results. VIs of all periods except 1 and 3 provided the best multi-temporal data set for winter wheat powdery mildew monitoring. Compared with the traditional single-date (period 6) image, the multi-temporal images based KNN approach provided more disease information during the disease development, and had an accuracy of 84.6%. Meanwhile, the accuracy of the proposed approach had 11.5% and 3.8% higher than the multi-temporal images-based CART and BPNN models', respectively. These results suggest that the use of satellite images for early critical disease infection periods is essential for improving the accuracy of monitoring models. Additionally, satellite imagery also assists in monitoring powdery mildew in late wheat growth periods.

Effects of irrigation water salinity on evapotranspiration modified by leaching fractions in hot pepper plants.

We investigated whether leaching fraction (LF) is able to modify the effects of irrigation water salinity (ECiw) on evapotranspiration (ET). We conducted an experiment with a completely randomized block design using five levels of ECiw and two LFs. Results showed that the electrical conductivity of drainage water (ECdw) in an LF of 0.29 was considerably higher during the 21-36 days after transplanting (DAT), and considerably lower after 50 DAT than in an LF of 0.17. The hourly, nighttime, daily, cumulative and seasonal ET all decreased considerably as a result of an increase in the ECiw. The daily ET started to be considerably higher in the LF of 0.29 than in the LF of 0.17 from 65 DAT. Compared with the LF of 0.17, the seasonal ET in the LF of 0.29 under various ECiw levels increased by 4.8%-8.7%. The Maas and Hoffman and van Genuchten and Hoffman models both corresponded well with the measured relative seasonal ET and the LF had no marked effects on these model parameters. Collectively, an increase in the level of ECiw always decreased the ET substantially. An increase in the LF increased the ET considerably, but there was a time lag.

Heat and mortality for ischemic and hemorrhagic stroke in 12 cities of Jiangsu Province, China.

BACKGROUND: Little evidence exists on the relationship between heat and subtypes of stroke mortality, especially in China. Moreover, few studies have reported the effect modification by individual characteristics on heat-related stroke mortality. In this study, we aimed to evaluate the effect of heat exposure on total, ischemic, and hemorrhagic stroke mortality and its individual modifiers in 12 cities in Jiangsu Province, China during 2009 to 2013. METHODS: We first used a distributed lag non-linear model with quasi-Poisson regression to examine the city-specific heat-related total, ischemic, and hemorrhagic stroke mortality risks at 99th percentile vs. 75th percentile of daily mean temperature in the whole year for each city, while adjusting for long-term trend, season, relative humidity, and day of the week. Then, we used a random-effects meta-analysis to pool the city-specific risk estimates. We also considered confounding by air pollution and effect modification by gender, age, education level, and death location. RESULTS: Overall, the heat-related mortality risk in 12 Jiangsu cities was 1.54 (95%CI: 1.44 to 1.65) for total stroke, 1.63 (95%CI: 1.48 to 1.80) for ischemic stroke, and 1.36 (95%CI: 1.26 to 1.48) for hemorrhagic stroke, respectively. Estimated total, ischemic, and hemorrhagic stroke mortality risks were higher for women versus men, older people versus younger people, those with low education levels versus high education levels, and deaths that occurred outside of hospital. Air pollutants did not significantly influence the heat-related stroke mortality risk. CONCLUSIONS: Heat exposure significantly increased both ischemic and hemorrhagic stroke mortality risks in Jiangsu Province, China. Females, the elderly, and those with low education levels are particularly vulnerable to this effect.

Response of evapotranspiration to changes in land use and land cover and climate in China during 2001-2013.

Land surface evapotranspiration (ET) is a central component of the Earth's global energy balance and water cycle. Understanding ET is important in quantifying the impacts of human influences on the hydrological cycle and thus helps improving water use efficiency and strengthening water use planning and watershed management. China has experienced tremendous land use and land cover changes (LUCC) as a result of urbanization and ecological restoration under a broad background of climate change. This study used MODIS data products to analyze how LUCC and climate change affected ET in China in the period 2001-2013. We examined the separate contribution to the estimated ET changes by combining LUCC and climate data. Results showed that the average annual ET in China decreased at a rate of -0.6mm/yr from 2001 to 2013. Areas in which ET decreased significantly were mainly distributed in the northwest China, the central of southwest China, and most regions of south central and east China. The trends of four climatic factors including air temperature, wind speed, sunshine duration, and relative humidity were determined, while the contributions of these four factors to ET were quantified by combining the ET and climate datasets. Among the four climatic factors, sunshine duration and wind speed had the greatest influence on ET. LUCC data from 2001 to 2013 showed that forests, grasslands and croplands in China mutually replaced each other. The reduction of forests had much greater effects on ET than change by other land cover types. Finally, through quantitative separation of the distinct effects of climate change and LUCC on ET, we conclude that climate change was the more significant than LULC change in influencing ET in China during the period 2001-2013. Effective water resource management and vegetation-based ecological restoration efforts in China must consider the effects of climate change on ET and water availability.

[Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat].

In order to improve the accuracy of wheat yellow rust disease severity using remote sensing and to find the optimum inversion model of wheat diseases, the canopy reflectance and disease index (DI) of winter wheat under different severity stripe rust were acquired. The three models of PLS (Partial Least Square), BP neural network using seven hyperspectral vegetation indices which have significant relationship with the occurrence of disease and vegetation index (PRI) were adopted to build a feasible regression model for detecting the disease severity. The results showed that PLS performed much better. The inversion accuracy of PLS method is best than of the VI (PRI, Photochemical Reflectance Index) and BP neural network models. The coefficients of determination (R2) of three methods to estimate disease severity between predicted and measured values are 0.936, 0.918 and 0.767 respectively. Evaluation was made between the estimated DI and the measured DI, indicating that the model based on PLS is suitable for monitoring wheat disease. In addition, to explore the different contributions of diverse types of vegetation index to the models, the paper attempts to use NDVI, GNDVI and MSR which on behalf of vegetation greenness and NDWI and MSI that represents the moisture content to be input variables of PLS model. The results showed that, for the wheat yellow rust disease, changes in chlorophyll content is more sensitive to the disease severity than the changes in water content of the canopy. However, the accuracy of the two models are both lower than predicted when participating in all seven vegetation indices, namely using several species of vegetation indices tends to be more accurate than that using single category. It indicated that it has great potential for evaluating wheat disease severity by using hyper-spectral remote sensing.

[Inversion of LAI by considering the hotspot effect for different geometrical wheat].

Aimed to deal with the limitation of canopy geometry to crop LAI inversion accuracy a new LAI inversion method for different geometrical winter wheat was proposed based on hotspot indices with field-measured experimental data. The present paper analyzed bidirectional reflectance characteristics of erective and loose varieties at red (680 nm) and NIR wavelengths (800 nm and 860 nm) and developed modified normalized difference between hotspot and dark-spot (MNDHD) and hotspot and dark-spot ratio index (HDRI) using hotspot and dark-spot index (HDS) and normalized difference between hotspot and dark-spot (NDHD) for reference. Combined indices were proposed in the form of the product between HDS, NDHD, MNDHD, HDRI and three ordinary vegetation indices NDVI, SR and EVI to inverse LAI for erective and loose wheat. The analysis results showed that LAI inversion accuracy of erective wheat Jing411 were 0.9431 and 0.9092 retrieved from the combined indices between NDVI and MNDHD and HDRI at 860 nm which were better than that of HDS and NDHD, the LAI inversion accuracy of loose wheat Zhongyou9507 were 0.9648 and 0.8956 retrieved from the combined indices between SR and HDRI and MNDHD at 800 nm which were also higher than that of HDS and NDHD. It was finally concluded that the combined indices between hotspot-signature indices and ordinary vegetation indices were feasible enough to inverse LAI for different crop geometrical wheat and multiangle remote sensing data was much more advantageous than perpendicular observation data to extract crop structural parameters.

[Recovery growth of Microcystis aeruginosa after the sub-high temperature stress].

Recovery growth of Microcystis aeruginosa after sub-high temperature stress was investiga- ted in this paper. The treated groups under 35 °C were cultured for 3, 6, and 12 days before being transferred to normal conditions, and the algae under 25 °C all the time was set as the control. Cell density, chlorophyll a, carotenoid, malondialdehyde and antioxidant enzymes activities were measured. The results showed that the growth of M. aeruginosa was inhibited significantly under the sub-high temperature stress. The cell density and chlorophyll a content were 14.5% and 22.3% lower than the control respectively on the 12th day, but carotenoid synthesis was not inhibited obviously. The longer the stress was, the higher the malondialdehyde content and SOD, CAT activities became. After the relief of stress, algal cells got recovered with the decreasing malondialdehyde content and antioxidase activities. The 3-, 6- and 12-day stress treatments at 35 °C showed under-compensation, over-compensation and exact-compensation, respectively, indicating that the com- pensatory degree was decided by the time under stress. As the recovery time proceeded, the differ- ence between treated groups and the control reduced gradually. The growth parameters tended to be stable. Regression equations of cell density and chlorophyll a changing with the stress time and recovery time were revealed. The compensation effect of M. aeruginosa was similar to the process of algal bloom. According to this endogenous biological characteristic, this study provided a theoretical support for prediction system of algal bloom.

[Inversion of leaf area index during different growth stages in winter wheat].

Being orientated to the low prescion of crop leaf area index (LAI) inversion using the same spectral vegetation index during different crop growth stages, the present paper analyzed the precision of LAI inversion by employing NDVI(normalized difference vegetation index). Ten vegetation indices were chosen including six broad-band vegetation indices and four narrow-band vegetation indices responding to vegetation cover to inverse LAI in different growth stages. Several conclusions were drawn according to the analysis. The determinant coefficient (R2) and root mean square error (RMSE) between LAI inversion value and true value were 0.5585 and 0.3209 respectively during the whole growth duraton. The mSR (modified simple ratio index) index was appropriate to inverse of LAI during earlier growth stages (before jointing stage) in winter wheat. The R2 and RMSE between LAI inversion value and true value were 0.7287 and 0.2971 respectively. The SR (simple ratio index) index was suitable enough to inverse of LAI during medium growth stages (from joingting stagess to heading stages). The R2 and RMSE between LAI inversion value and true value were 0.6546 and 0.3061 respectively. The NDVI (normalized difference vegetation index) index was proven to be fine to inverse LAI during later growth stages(from heading stage to ripening stage). The R2 and RMSE between LAI inversion value and true value were 0.6794 and 0.3164 respectively. Therefore it was indicated that the results of LAI inversion was much better inverse of winter wheat LAI choosing different vegetation indices during differen growth stages for winter wheat according to the change of vegetation cover and canopy reflectance than merely with NDVI to inverse LAI in the whole growth stages. It was concluded that the precision of LAI inversion was significantly improved with segmented models based on different vegetation indices.