Powdery mildew of wheat, caused by
f. sp.
(
), is a devastating disease that seriously reduces yield and quality worldwide. Utilization of plant resistance genes is an attractive and effective ...strategy for controlling this disease. Among the reported powdery mildew (
) resistance genes,
exhibits a diverse resistance spectrum among its multiple alleles. It has been widely used in China for resistance breeding for powdery mildew. To mine more
alleles and clarify their distribution, we screened 33 wheat cultivars/breeding lines carrying
alleles from 641 wheat genotypes using diagnostic and
-linked markers. To further investigate the relationships within the
alleles, we compared their resistance spectra, polymorphism of marker alleles and gene sequences, and found that they have identical marker alleles and gene sequences but diverse resistance spectra. In addition, the diagnostic kompetitive allele-specific PCR (KASP) marker,
, was developed and was shown to detect all the
alleles in the different genetic backgrounds. These findings provide valuable information for the distribution and rational use of
alleles, push forward their marker-assisted breeding (MAS), and hence improve the control of wheat powdery mildew.
Air Pollution and Covid-19 Catarina Ferrão; Ricardo Almendra
Cadernos de geografia (Coimbra, Portugal),
05/2024, Volume:
49
Journal Article
Peer reviewed
Open access
Air quality stands out as an important determinant of health, as its degradation was associated with around 4.2 million premature deaths in 2019, primarily due to heart and respiratory problems. It ...is shown that the elderly, the children, and individuals with pre-existing health conditions are simultaneously more sensitive to the impacts of air pollution and Covid-19, due to their fragile immune systems. Scientific evidence has shown the consequences of exposure to air pollutants to respiratory system diseases, emphasizing that it could be an important factor in explaining the spatial pattern of Covid-19 incidence and mortality. The aim of this study is to analyze the spatial association between air pollutant PM₂.₅ and the incidence and mortality of Covid-19 during March and December of 2020 in mainland Portugal. Weighted geographical models (GWR), were applied to identify and understand patterns, as well as explanatory factors in this relationship. The results obtained through the GWR models reveal that the pollutant PM₂.₅ has an association that varies in space. The incidence rate is higher in the southern, central, and northern regions of the country. The results of this study contribute to the analysis and assessment of the impact of air pollutants on human health, specifically in relation to health outcomes associated with Covid-19. It became evident that the concentration of PM₂.₅ is an important factor in explaining Covid-19 incidence rate in Portugal.
Concern over the health effects of fine particles in the ambient environment led the U.S. Environmental Protection Agency to develop the first standard for PM2.5 （particulate matter less than 2.5 μm） ...in 1997. The Particle Technology Laboratory at the University of Minnesota has helped to establish the PM2.5 standard by developing many instruments and samplers to perform atmospheric measurements. In this paper, we review various aspects of PM2.5, including its measurement, source apportionment, visibility and health effects, and mitigation. We focus on PM2.s studies in China and where appropriate, compare them with those obtained in the U.S. Based on accurate PM2.5 sampling, chemical analysis, and source apportionment models, the major PM2.5 sources in China have been identified to be coal combustion, motor vehicle emissions, and industrial sources. Atmospheric visibility has been found to correlate well with PM2.s concentration. Sulfate, ammonium, and nitrate carried by PM2.s, commonly found in coal burning and vehicle emissions, are the dominant contributors to regional haze in China. Short-term exposure to PM2.s is strongly associated with the increased risk of morbidity and mortality from cardiovascular and respiratory diseases in China. The strategy for PMzs mitigation must be based on reducing the pollutants from the two primary sources of coal-fired power plants and vehicle emissions. Although conventional Particulate Emission Control Devices （PECD） such as electrostatic precipitators in Chinese coal-fired power plants are generally effective for large particles, most of them may not have high collection efficiency of PM2.5. Baghouse filtration is gradually incorporated into the PECD to increase the PM2.5 collection efficiency. By adopting stringent vehicle emissions standard such as Euro 5 and 6, the emissions from vehicles can be gradually reduced over the years. An integrative approach, from collaboration among academia, government, and industries, can effectively manage and mitigate the PM2.s pollution in China.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Pollution due to particulate matter can harm human health and the environment. One of the particulate matter sources is the mining industry. Depending on the type of activity (drilling, blasting, ...loading, hauling, dozing, crushing, screening, etc.), various levels of PM emission occurs in surface mining operations. For this reason, it is important to measure the PM emission values of each ongoing activity, evaluate them by taking into consideration the limit values specified in the relevant legislation and to plan preventive/mitigating measures. When the studies conducted on the subject were examined, it was found out that the equations modeling the PM emission, which may occur according to the type of activity carried out in surface mines, were developed but they varied even for the same activity among different mines. For this reason, it was concluded that PM emission values are specific to the surface mines, that continuous emission measurement will provide more accurate results, and therefore, as the most realistic approach, separate PM emission modeling should be done for each activity carried out in a surface mine.
Due to their extensive spatial coverage, satellite Aerosol Optical Depth (AOD) observations have been widely used to estimate and predict surface PM2.5 concentrations. While most previous studies ...have focused on establishing relationships between collocated, hourly or daily AOD and PM2.5 measurements, in this study, we instead focus on the comparison of the large-scale spatial and temporal variability between satellite AOD and PM2.5 using monthly mean measurements. A newly developed spectral analysis technique – Combined Maximum Covariance Analysis (CMCA) is applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Ozone Monitoring Instrument (OMI) AOD datasets and Environmental Protection Agency (EPA) PM2.5 data, in order to extract and compare the dominant modes of variability. Results indicate that AOD and PM2.5 agree well in terms of interannual variability. An overall decrease is found in both AOD and PM2.5 across the United States, with the strongest signal over the eastern US. With respect to seasonality, good agreement is found only for Eastern US, while for Central and Western US, AOD and PM2.5 seasonal cycles are largely different or even reversed. These results are verified using Aerosol Robotic Network (AERONET) AOD observations and differences between satellite and AERONET are also examined. MODIS and MISR appear to have the best agreement with AERONET. In order to explain the disagreement between AOD and PM2.5 seasonality, we further use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) extinction profile data to investigate the effect of two possible contributing factors, namely aerosol vertical distribution and cloud-free sampling. We find that seasonal changes in aerosol vertical distribution, due to the seasonally varying mixing height, is the primary cause for the AOD and PM2.5 seasonal discrepancy, in particular, the low AOD but high PM2.5 observed during the winter season for Central and Western US. In addition, cloud-free sampling by passive sensors also induces some bias in AOD seasonality, especially for the Western US, where the largest seasonal change in cloud fraction is found. The seasonal agreement between low level (below 500 m AGL), all sky CALIOP AOD and PM2.5 is significantly better than column AOD from MODIS, MISR, SeaWiFS and OMI. In particular, the correlation between low level, all sky AOD and PM2.5 seasonal cycles increases to above 0.7 for Central and Western US, as opposed to near zero or negative correlation for column, clear sky AOD. This result highlights the importance of accounting for the seasonally varying aerosol profiles and cloud-free sampling bias when using column AOD measurements to infer surface PM2.5 concentrations.
•Several spectral analysis techniques were used to examine spatial and temporal variability.•AOD and PM2.5 variability agree well for East US but disagree for Central and West US.•Aerosol vertical distribution is a major factor in the AOD-PM2.5 relationship.•Cloud-free sampling by passive sensors may also decrease AOD-PM2.5 correlation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The Portuguese Project Management Association (APOGEP) launched in September 2020 the project of implementation of the Portuguese Project Management Observatory (PPMO), a non-profit organization with ...a focus on the status and evolution of project management in Portugal. The project is being managed using the PM² methodology, developed by the European Commission (EC) that has been available, since 2016, free of charge to the general public. The methodology has few scientific references, so this project was used to contribute to filling this gap. The methodology incorporates best practices from other bodies of knowledge, can be easily applied and allows for customization. The initiating phase has been completed and the details of its implementation are focused on in this paper. The client involvement from an early stage was crucial and it was necessary to use more artefacts than those mentioned for this phase. Therefore, this paper presents the tailoring of the PM² methodology to the PPMO project, for the initiating phase.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
After literally half a century of studies conducted in the western countries on the levels of pollutants in the atmosphere and their effects on human health, it is scientifically confirmed that a low ...quality of air quality leads to a state of Poor people's health. Particulate particles are criminated to have the most severe negative impact on the health of the population. In Romania it was updated according to European requirements, legislation in the field, by the adoption by the Romanian Parliament of Law No. 104 of 15 June 2011 on ambient air quality. This normative act aims to ensure support for the protection of human health and the environment, as a whole, by regulating measures intended to preserve ambient air quality, where this corresponds to the objectives for the quality Ambient air set out in that link.
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•Exposure inequality was contrary between ambient and household PM2.5 exposure.•The poorest experienced the highest PM2.5 exposure though ambient exposure was low.•Negative ...concentration index gradually increased indicating a transition towards equality.•Aggressive countermeasures reduced ambient exposures but benefited people disproportionately.•Spontaneous clean household energy transition effectively reduced the exposure inequality.
The society has high concerns on the inequality that people are disproportionately exposed to ambient air pollution, but with more time spent indoors, the disparity in the total exposure considering both indoor and outdoor exposure has not been explored; and with the socioeconomical development and efforts in fighting against air pollution, it is unknown how the exposure inequality changed over time. Based on the city-level panel data, this study revealed the Concentration Index (C) in ambient PM2.5 exposure inequality was positive, indicating the low-income group exposed to lower ambient PM2.5; however, the total PM2.5 exposure was negatively correlated with the income, showing a negative C value. The low-income population exposed to high PM2.5 associated with larger contributions of indoor exposure from the residential emissions. The total PM2.5 exposure caused 1.13 (0.63–1.73) million premature deaths in 2019, with only 14 % were high-income population. The toughest-ever air pollution countermeasures have reduced ambient PM2.5 exposures effectively that, however, benefited the rich population more than the others. The transition to clean household energy sources significantly affected on indoor air quality improvements, as well as alleviation of ambient air pollution, resulting in notable reductions of the total PM2.5 exposure and especially benefiting the low-income groups. The negative C values decreased from 2000 to 2019, indicating a significantly reducing trend in the total PM2.5 exposure inequality over time.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
With the development of the data-driven modeling techniques, using the neural network to simulate the transport process of atmospheric pollutants and constructing <inline-formula> <tex-math ...notation="LaTeX">{\textrm {PM}}_{2.5} </tex-math></inline-formula> time-series prediction model have become a hot topic. The existing data-driven approaches often ignore the dynamical relationships among multiple sites in urban areas, which results in nonideal prediction accuracy. In response to this problem, this article proposes a long short-term memory (LSTM) autoencoder multitask learning model to predict <inline-formula> <tex-math notation="LaTeX">{\textrm {PM}}_{2.5} </tex-math></inline-formula> time series in multiple locations city wide. The model could implicitly and automatically excavate the intrinsic relevance among the pollutants in different stations. And the meteorological information from the monitoring stations is fully utilized, which is beneficial for the performance of the proposed model. Specifically, multilayer LSTM networks can simulate the spatiotemporal characteristics of urban air pollution particles. And using the stacked autoencoder to encode the key evolution pattern of urban meteorological systems could provide important auxiliary information for <inline-formula> <tex-math notation="LaTeX">{\textrm {PM}}_{2.5} </tex-math></inline-formula> time-series prediction. In addition, multitask learning could automatically discover the dynamical relationship between multiple key pollution time series and solve the problem of insufficient use of multisite information in the modeling process of the traditional data-driven methods. The simulation results of <inline-formula> <tex-math notation="LaTeX">{\textrm {PM}}_{2.5} </tex-math></inline-formula> prediction in Beijing indicate the effectiveness of the proposed method.
The devastating effects of COVID-19 pandemic have widely affected human lives and economy across the globe. There were significant changes in the global environmental conditions in response to the ...lockdown (LD) restrictions made due to COVID-19. The direct impact of LD on environment is analysed widely across the latitudes, but its secondary effect remains largely unexplored. Therefore, we examine the changes in particulate matter (PM₂.₅) during LD, and its impact on the global croplands. Our analysis finds that there is a substantial decline in the global PM₂.₅ concentrations during LD (2020) compared to pre-lockdown (PreLD: 2017–2019) in India (10–20%), East China (EC, 10%), Western Europe (WE, 10%) and Nigeria (10%), which are also the cropland dominated regions. Partial correlation analysis reveals that the decline in PM₂.₅ positively affects the cropland greening when the influence of temperature, precipitation and soil moisture are limited. Croplands in India, EC, Nigeria and WE became more greener as a result of the improvement in air quality by the reduction in particulates such as PM₂.₅ during LD, with an increase in the Enhanced Vegetation Index (EVI) of about 0.05–0.1, 0.05, 0.05 and 0.05–0.1, respectively. As a result of cropland greening, increase in the total above ground biomass production (TAGP) and crop yield (TWSO) is also found in EC, India and Europe. In addition, the improvement in PM₂.₅ pollution and associated changes in meteorology also influenced the cropland phenology, where the crop development stage has prolonged in India for wet-rice (1–20%) and maize (1–10%). Therefore, this study sheds light on the response of global croplands to LD-induced improvements in PM₂.₅ pollution. These finding have implications for addressing issues of air pollution, global warming, climate change, environmental conservation and food security to achieve the Sustainable Development Goals (SDGs).
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•Negative link between PM2.5 and EVI is found in the global croplands during lockdown.•Significant reduction in PM₂.₅ is observed during LD, with hotspots in India and East China.•Enhanced cropland greening in India, China and WE due to the decrease in PM2.5•Lockdown induced improvement in air pollution can be a lesson for attaining SDGs.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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