scholarly journals Needle traces as indicators of growing conditions in Scots pine (Pinus sylvestris L.)

2012 ◽  
Vol 49 (No. 1) ◽  
pp. 1-10 ◽  
Author(s):  
B. Konôpka
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Pinus Sylvestris ◽  
Forest Stand ◽  
Needle Retention ◽  
Increasing Trend ◽  
Forest Regions ◽  
Needle Trace Method ◽  
Growing Conditions

Needle retention (number of needle sets) and needle density (number of needle pairs per centimeter of shoot) were surveyed on Scots pines in five forest regions of Slovakia. The Needle Trace Method (NTM) was used to determine needle retention and needle density along the main stem retrospectively for the last four decades. In all forest regions, the values of these indicators varied from year to year. However, in Záhorská lowland, Vtáčnik, Krupinská plain, and High Tatras, the trends of both observed indicators were constant over the time series. The situation was different in the Levočské hills, where the needle retention displayed a decreasing trend and needle density an increasing trend. These trends probably reflected a long-term stress of air pollution on pines in this forest stand.

scholarly journals Air pollution, SARS-CoV-2 transmission, and COVID-19 outcomes: A state-of-the-science review of a rapidly evolving research area

2020 ◽  
Author(s):  
Anushka Bhaskar ◽  
Jay Chandra ◽  
Danielle Braun ◽  
Jacqueline Cellini ◽  
Francesca Dominici
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Health Outcomes ◽  
Study Design ◽  
Air Pollutant ◽  
Short Term ◽  
Cross Sectional ◽  
Short And Long Term ◽  
Long Term Studies

Background: As the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis. Objectives: To conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes. Method: We searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design. Results: 27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes. Discussion: We discuss methodological challenges and highlight additional research areas based on our findings. Challenges include data quality issues, ecological study design limitations, improved adjustment for confounders, exposure errors related to spatial resolution, geographic variability in testing, mitigation measures and pandemic stage, clustering of health outcomes, and a lack of publicly available data and code.

Needle-retention and density patterns in Pinus sylvestris in the Rhone Valley of Switzerland: comparing results of the needle-trace method with visual defoliation assessments

2000 ◽  
Vol 30 (12) ◽  
pp. 1973-1982 ◽  
Author(s):  
Antti Pouttu ◽  
Matthias Dobbertin
Keyword(s):  
Pinus Sylvestris ◽  
Forest Health ◽  
Tree Age ◽  
Annual Change ◽  
Needle Retention ◽  
The Past ◽  
Needle Trace Method ◽  
The Mean ◽  
Short Shoots ◽  
Rhone Valley

We used the needle-trace method (NTM) to reveal the needle-retention patterns of Scots pine (Pinus sylvestris L.) over the past 100 years. The average annual needle retention (ANR) on main stems has gradually decreased from five needle sets in the 1890s to four needle sets in the 1990s. Needle retention is significantly correlated with tree age and altitude, and the decrease in needle retention may be at least in part due to the increasing ages of sample trees. The average needle density varied plotwise between 7.2 and 10.5 short shoots/cm. In a comparison of ANR values and visually assessed foliage percentage from the Swiss Forest Health Inventory between 1985 and 1996, we found significant correlation between the mean annual values. While the direction of annual change was identical in two thirds of all years we found disagreement in 3 years. Both needle retention and foliage were lower in the early 1990s than in the late 1980s. With the help of the NTM we can show that there had been similar decreases in foliage usually connected with severe droughts during the last century.

scholarly journals Forecasting Tourist Arrival in the Province of Surigao del Sur, Philippines using Time Series Analysis

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Ruby Mae Ebuna Maliberan
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Forecast Model ◽  
Mean Absolute Percentage Error ◽  
Actual Data ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Increasing Trend ◽  
The Mean

The study attempted to forecast the number of tourist arrival in the province of Surigao del Sur using the historical monthly tourist arrival data from 2012-2016 using three time series. Findings showed that the tourist arrival in the province is likely to be increasing. As more foreign and local tourist arrivals are expected as a result of forecast model. Furthermore, it showed that there was a long term increasing trend of the tourist arrival in the province. Results revealed that the Mean Absolute Percentage Error (MAPE) of the forecasted tourist arrival data yielded an error of 11 % which means that predicted data is closer to the actual data. Based on the findings of the study, the researcher recommends that this study can be adapted by other Tourism Office of CARAGA, Philippines. 

scholarly journals Air Pollution and Mortality: Timing Is Everything

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1274
Author(s):  
Frederick W. Lipfert
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Indoor Air Pollution ◽  
Health Effect ◽  
Traffic Density ◽  
Long Term Effects ◽  
Short Term ◽  
Long Term Studies

This paper considers timing issues in health-effect exposure and response studies. Short-term studies must consider delayed and cumulative responses; prior exposures, disease latency, and cumulative impacts are required for long-term studies. Lacking individual data, long-term air quality describes locations, as do greenspaces and traffic density, rather than exposures of residents. Indoor air pollution can bias long-term exposures and effect estimates but short-term effects also respond to infiltrated outdoor air. Daily air quality fluctuations may affect the frail elderly and are necessarily included in long-term averages; any true long-term effects must be given by differences between annual and daily effects. I found such differences to be negligible after adjusting for insufficient lag effects in time-series studies and neglect of prior exposures in long-term studies. Aging of subjects under study implies cumulative exposures, but based on age-specific mortality, I found relative risks decreasing with age, precluding cumulative effects. A new type of time-series study found daily mortality of previously frail subjects to be associated with various pollutants without exposure thresholds, but the role of air pollution in the onset of frailty remains an unexplored issue. The importance of short-term fluctuations has been underestimated and putative effects of long-term exposures have been overestimated.

scholarly journals Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

2022 ◽  
Author(s):  
Zhen Zhang ◽  
Shiqing Zhang ◽  
Xiaoming Zhao ◽  
Linjian Chen ◽  
Jun Yao
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Attention Mechanism ◽  
Quality Prediction ◽  
Temporal Difference ◽  
Short Term ◽  
Air Quality Prediction ◽  
Complex Relationships

Abstract The acceleration of industrialization and urbanization has recently brought about serious air pollution problems, which threaten human health and lives, the environmental safety, and sustainable social development. Air quality prediction is an effective approach for providing early warning of air pollution and supporting cleaner industrial production. However, existing approaches have suffered from a weak ability to capture long-term dependencies and complex relationships from time series PM2.5 data. To address this problem, this paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then, based on the constructed graph, we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. To verify the effectiveness of the proposed method, we conduct air quality prediction experiments on two real-world datasets in China, such as Beijing PM2.5 dataset ranging from 01/01/2010 to 12/31/2014 and Taizhou PM2.5 dataset ranging from 01/01/2017 to 12/31/2019. Compared with other air quality forecasting methods, such as autoregressive moving average (ARMA), support vector regression (SVR), convolutional neural network (CNN), long short-term memory (LSTM), the original Transformer, our experiment results indicate that the proposed method achieves more accurate results on both short-term (1 hour) and long-term (6, 12, 24, 48 hours) air quality prediction tasks.

scholarly journals Long-Term Homogeneity and Trends of Hydroclimatic Variables in Upper Awash River Basin, Ethiopia

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Mekonnen H. Daba ◽  
Gebiaw T. Ayele ◽  
Songcai You
Keyword(s):  
Time Series ◽  
Water Resource ◽  
Addis Ababa ◽  
Annual Rainfall ◽  
Homogeneity Test ◽  
Homogeneity Tests ◽  
Sen’S Slope ◽  
Increasing Trend ◽  
Hydroclimatic Variables

Understanding long-term trends in hydroclimatic variables is important for future sustainable water resource management as it could show the possible regime shifts in hydrology. The main objective of this study was to analyze the homogeneity and trends of hydroclimatic data of Upper Awash Sab-Basin (UASB) in Oromia, Ethiopia, by employing homogeneity tests and Mann-Kendall and Sen’s slope tests. The data consist of 18 rainfall stations, 8 temperature stations, and 8 flow gauging stations across the UASB. Homogeneity and trends in streamflow, rainfall, and temperature variables were analyzed for the time period 1980 to 2017. In order to analyze homogeneity of hydroclimatic variables, we used four homogeneity tests (Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test) at 5% significance level. Based on the outputs of four homogeneity tests, the results were classified into three categories, namely, “useful,” “doubtful,” and “suspect” to select the homogeneity stations. Mann-Kendall (Z) and Sen’s slope tests (Q) were applied for the selected homogeneous time series to detect the trend and magnitude of changes in hydroclimatic variables. The result showed that most of the stations in annual rainfall and streamflow data series were classified as useful. It is found that 58% of the rainfall stations were homogeneous. It is highlighted that 3 out of 8 discharge gauging stations have an inhomogeneity as they failed from one or a combination of the four tests. The MK revealed significant decreasing trends of annual rainfall in Addis Alem (Q = −19.81), Akaki (Q = −5.60), Hombole (Q = −9.49), and Ghinch (Q = −12.38) stations. The trend of annual temperature was a significant increasing trend in Addis Ababa Bole (Q = 0.05), Addis Ababa Tikur Ambessa (Q = 0.03), Tulu Bolo (Q = 0.07), and Addis Alem (Q = 0.06) stations. The results of discharge showed a significant increasing trend in Bega at Mojo (Q = 0.17) and Hombole (Q = 0.03) gauging stations. In general, the results obtained from discharge, rainfall, and temperature series indicated that most of the stations exhibited no trends in both annual and seasonal time series. It can be concluded that decreases in average annual rainfall totals and increases in mean annual temperature will probably drive sub-basin scale changes in discharge. We believe that the results obtained can fill information gaps on homogeneity and trends of hydroclimatic variables, which is very crucial for future water resource planning and management in the face of climate change.

Abstract P067: Long-term Exposure to Ambient Air Pollution and Type 2 Diabetes Incidence: A Time Series Analysis

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Guozhang Xu ◽  
Donghuui Duan ◽  
Dingyun You ◽  
Jiaying Xu ◽  
Xiaoqi Feng ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Air Pollution ◽  
Time Series ◽  
Air Pollutants ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Relative Risks ◽  
Ambient Air Pollutants

Introduction: Epidemiological evidence on long-term exposure to ambient air pollution and type 2 diabetes (T2D) incidence are sparse, and the results are contradictory. Hypothesis: We performed a time-series analysis to investigate potential association between long-term exposure to ambient air pollution and T2D incidence in the Chinese population. Methods: Monthly time-series data between 2008-2015 on ambient air pollutants and incident T2D were obtained from the Environment Monitoring Center of Ningbo and the Chronic Disease Surveillance System of Ningbo. Relative risks (RRs) and 95% confidence intervals (95%CIs) of incident T2D per 10 μg/m 3 increase in ambient air pollutants were estimated from Poisson generalized additive models and adjusted for month, temperature, relative humidity, air pressure and wind speed. This model was combined with a distributed lag non-linear model to determine the relative risks. Main Outcome Measures: The main outcome measure was T2D incidence. Results: Long-term exposure to particulate matter <10 μm (PM10) and Sulphur dioxide (SO2) were associated with increased T2D incidence. The relative risks (RRs) of each increment in 10 μg/m 3 of PM10 and SO2 were 1.62 (95%CI, 1.16 to 2.28) and 1.63 (95%CI, 1.12 to 2.38) for overall participants, 1.56 (95%CI, 1.12 to 2.17) and 1.59 (95%CI, 1.14 to 2.23) for males, 1.68 (95%CI, 1.15 to 2.44) and 1.76 (95%CI, 1.21 to 2.56) for females, respectively. Whereas for ozone (O3) exposure, the RRs were 0.78 (95%CI, 0.68 to 0.90) for overall participants, 0.78 (95%CI, 0.69 to 0.90) for males, and 0.78 (95%CI, 0.67 to 0.91) for females, respectively. Female participants were more prone to develop T2D after long-term exposed to ambient air pollutants than male counterparts. No statistically significant associations were observed for PM2.5, NO2, and CO exposures, nor in the two- and three-pollutant models. Conclusions: Long-term exposure to PM10 and SO2 is positively associated with T2D incidence, whereas O3 is negatively associated with T2D incidence.

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