Application of the Principal Component Analysis (PCA) Method to Assess the Impact of Meteorological Elements on Concentrations of Particulate Matter (PM10): A Case Study of the Mountain Valley (the Sącz Basin, Poland)

The aim of this study was to determine, by use PCA analysis, the impact of meteorological elements on the PM10 concentration on the example of the mountain valley. Daily values of selected meteorological elements, measured during a ten-year period in the spring, summer, autumn and winter, obtained from the meteorological station in Nowy Sącz, were adopted as variables explaining PM10 concentration. The level of PM10 was significantly affected by the maximum, minimum and average temperature in autumn, winter and spring. In summer the average and maximum temperature was significant. In winter, the first principle component mainly consisted of the combination of the average and maximum wind speed. The second principal component in spring, summer and autumn was the combination of the wind speed (average and maximum), but in winter humidity and atmospheric pressure seemed to be significant. The third principal component, in terms of strength of impact, was humidity in spring, the combination of humidity and minimum temperature in summer, and precipitation in autumn. In winter, the highest PM10 concentrations were observed during the non-directional, anticyclonic wedge conditions. Three principal components were distinguished in this situation: temperature (average, maximum and minimum); the combination of humidity and wind speed and precipitation.

Download Full-text

Sensitivity of wheat yields to rise in growing season temperature: Evidences from panel data analysis

Journal of Agrometeorology ◽

10.54386/jam.v22i2.167 ◽

2021 ◽

Vol 22 (2) ◽

pp. 191-197

Author(s):

K. PHILIP ◽

S.S. ASHA DEVI ◽

G.K. JHA ◽

B.M.K. RAJU ◽

B. SEN ◽

...

Keyword(s):

Climate Change ◽

Panel Data ◽

Growing Season ◽

Maximum Temperature ◽

Data Set ◽

Impact Of Climate Change ◽

Average Maximum ◽

Wide Range ◽

Potential Impact ◽

The Impact

The impact of climate change on agriculture is well studied yet there is scope for improvement as crop specific and location specific impacts need to be assessed realistically to frame adaptation and mitigation strategies to lessen the adverse effects of climate change. Many researchers have tried to estimate potential impact of climate change on wheat yields using indirect crop simulation modeling techniques. Here, this study estimated the potential impact of climate change on wheat yields using a crop specific panel data set from 1981 to 2010,for six major wheat producing states. The study revealed that 1°C increase in average maximum temperature during the growing season reduces wheat yield by 3 percent. Major share of wheat growth and yield (79%) is attributed to increase in usage of physical inputs specifically fertilizers, machine labour and human labour. The estimated impact was lesser than previously reported studies due to the inclusion of wide range of short-term adaptation strategies to climate change. The results reiterate the necessity of including confluent factors like physical inputs while investigating the impact of climate factors on crop yields.

Download Full-text

Challenges in estimating the impact of vaccination with sparse data

10.1101/302224 ◽

2018 ◽

Author(s):

Kayoko Shioda ◽

Cynthia Schuck-Paim ◽

Robert J. Taylor ◽

Roger Lustig ◽

Lone Simonsen ◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Simulation Analysis ◽

Principal Component ◽

Population Level ◽

Series Data ◽

Synthetic Control ◽

Vaccine Impact ◽

Pca Method ◽

The Impact

ABSTRACTBackgroundThe synthetic control (SC) model is a powerful tool to quantify the population-level impact of vaccines, because it can adjust for trends unrelated to vaccination using a composite of control diseases. Because vaccine impact studies are often conducted using smaller subnational datasets, we evaluated the performance of SC models with sparse time series data. To obtain more robust estimates of vaccine effects from noisy time series, we proposed a possible alternative approach, “STL+PCA” method (seasonal-trend decomposition plus principal component analysis), which first extracts smoothed trends from the control time series and uses them to adjust the outcome.MethodsUsing both the SC and STL+PCA models, we estimated the impact of 10-valent pneumococcal conjugate vaccine (PCV10) on pneumonia hospitalizations among cases <12 months and 80+ years of age during 2004-2014 at the subnational level in Brazil. The performance of these models was also compared using simulation analyses.ResultsThe SC model was able to adjust for trends unrelated to PCV10 in larger states but not in smaller states. The simulation analysis confirmed that the SC model failed to select an appropriate set of control diseases when the time series were sparse and noisy, thereby generating biased estimates of the impact of vaccination when secular trends were present. The STL+PCA approach decreased bias in the estimates for smaller populations.ConclusionsEstimates from the SC model might be biased when data are sparse. The STL+PCA model provides more accurate evaluations of vaccine impact in smaller populations.

Download Full-text

What Role Does Financial Inclusion Play for Poverty Reduction? Empirical Evidence From The Indian States in the Post-Liberalization Era

10.21203/rs.3.rs-217570/v1 ◽

2021 ◽

Author(s):

Rajesh Barik ◽

Sanjaya Kumar Lenka

Keyword(s):

Poverty Reduction ◽

Urban Poverty ◽

Random Effect ◽

Principal Component ◽

Financial Inclusion ◽

Least Square ◽

Indian States ◽

Pca Method ◽

Using Data ◽

The Impact

Abstract The paper tries to analyzes the effect of financial inclusion on poverty reduction among 28 Indian states and rural-urban as well. Using data from 28 Indian states over the period of 1993 to 2015, this study constructed a single financial inclusion index through Principal Component Analysis (PCA) method, which signifies the state-wise variation in financial inclusion services. Furthermore, this study uses Fixed Effect, Random Effect, Panel Corrected Standard Errors, Feasible General Least Square, and Hausman-Taylor Regression model to know the impact of financial inclusion on state-wise poverty reduction and rural-urban poverty reduction as well. The results of this study suggest that financial inclusion has a negative and significant effect on state-wise and rural-urban poverty reduction respectively. With regards to the control variables, this study finds that variables like social sector expenditure, per capita state GDP and capital receipt are negatively associated with all three categories of poverty (i.e., overall poverty and rural-urban poverty) whereas the rural population is positively associated.

Download Full-text

Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis

Scientific Reports ◽

10.1038/s41598-021-85928-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ranjita Sinha ◽

Vadivelmurugan Irulappan ◽

Basavanagouda S. Patil ◽

Puli Chandra Obul Reddy ◽

Venkategowda Ramegowda ◽

...

Keyword(s):

Soil Moisture ◽

Root Rot ◽

Disease Incidence ◽

Maximum Temperature ◽

Validation Dataset ◽

Water Deficit Stress ◽

Weather Factors ◽

Rhizoctonia Bataticola ◽

Average Maximum ◽

The Impact

AbstractRhizoctonia bataticola causes dry root rot (DRR), a devastating disease in chickpea (Cicer arietinum). DRR incidence increases under water deficit stress and high temperature. However, the roles of other edaphic and environmental factors remain unclear. Here, we performed an artificial neural network (ANN)-based prediction of DRR incidence considering DRR incidence data from previous reports and weather factors. ANN-based prediction using the backpropagation algorithm showed that the combination of total rainfall from November to January of the chickpea-growing season and average maximum temperature of the months October and November is crucial in determining DRR occurrence in chickpea fields. The prediction accuracy of DRR incidence was 84.6% with the validation dataset. Field trials at seven different locations in India with combination of low soil moisture and pathogen stress treatments confirmed the impact of low soil moisture on DRR incidence under different agroclimatic zones and helped in determining the correlation of soil factors with DRR incidence. Soil phosphorus, potassium, organic carbon, and clay content were positively correlated with DRR incidence, while soil silt content was negatively correlated. Our results establish the role of edaphic and other weather factors in chickpea DRR disease incidence. Our ANN-based model will allow the location-specific prediction of DRR incidence, enabling efficient decision-making in chickpea cultivation to minimize yield loss.

Download Full-text

Impact of seasonal climatic variability on rice yield in Bangladesh

Journal of Agrometeorology ◽

10.54386/jam.v22i2.157 ◽

2021 ◽

Vol 22 (2) ◽

pp. 165-171

Author(s):

JAIONTO KARMOKAR ◽

M. AMINUL ISLAM ◽

M. RAKIB HASSAN ◽

M.M. BILLAH

Keyword(s):

Rice Yield ◽

Climatic Variability ◽

Vital Role ◽

Least Square ◽

Maximum Temperature ◽

Negative Effects ◽

Positive Effects ◽

Average Maximum ◽

The Impact ◽

Boro Rice

In Bangladesh, 75% of the total cultivable area is under rice cultivation producing 25 million tons of rice and plays a vital role in the country’s GDP. The climatic variability is playing an important role in affecting the rice production. In this study, the impact of climatic variability (average maximum temperature (aMaxTemp), average minimum temperature (aMinTemp) and average rainfall (aRainfall)) on rice yield was determined in two different regions (northern and southern) of Bangladesh.The variability of rice yield and climate factors was determined by using the Ordinary Least Square (OLS) method. The data was analyzed over the 44-years period (1971 to 2014) in order to estimate the magnitude of these fluctuations statistically and graphically. We observed that the climate variables had significant effect on rice yield that varies among three rice crops (e.g., Aus, Aman, and Boro rice). We observed that, aMaxTemp has positive effects for Aus and Aman rice yield but negative effect on Boro rice yield. On the other hand, aMinTemp has negative effects on Aus and Aman rice yield but has positive effect on Boro rice yield. The aRainfall has a positive relationship with all rice yields in both the regions.

Download Full-text

The course of birch pollen seasons in Sosnowiec (Southern Poland) in 1997–2020 in relation to meteorological conditions

Aerobiologia ◽

10.1007/s10453-021-09734-3 ◽

2021 ◽

Author(s):

Katarzyna Dąbrowska-Zapart ◽

Tadeusz Niedźwiedź

Keyword(s):

Wind Speed ◽

Pollen Season ◽

Birch Pollen ◽

Pollen Grains ◽

Meteorological Conditions ◽

Maximum Temperature ◽

Synoptic Situation ◽

Pollen Concentrations ◽

Pollen Seasons ◽

The Impact

AbstractThe study's main objective was to specify the extent to which weather conditions were related to the course of birch pollen seasons in the years 1997–2020. The impact of atmospheric conditions on the daily concentrations of birch pollen grains, the Annual pollen integral (APIn), and the length of pollen seasons were studied. The dependency between each meteorological condition and various features of the birch pollen season was determined using Spearman’s rho correlation, the Kruskal–Wallis test, and cluster analysis with the k-means method. It has been shown that the duration of sunshine and average air temperature occurring within 14 days preceding the season has the most significant influence on the beginning of a birch pollen season. The value of daily birch pollen concentrations in Sosnowiec showed a statistically significant positive correlation with the duration of sunlight and the average and maximum wind speed. The daily concentration also depended on the synoptic situation: the mass airflow direction, the type of air mass inflow, and the type of weather front. The near-ground temperature influenced the APIn of birch pollen grains during the period of 14 days before the beginning of the season and the meteorological conditions occurring in the summer of the preceding year such as the maximum temperature, duration of sunlight, the maximum and average wind speed, and the relative air humidity. It was concluded that the length of birch pollen seasons decreased year by year.

Download Full-text

Characteristics of thunderstorms and squalls over Indira Gandhi International (IGI) airport, New Delhi - Impact on environment especially on summer’s day temperatures and use in forecasting

MAUSAM ◽

10.54302/mausam.v60i4.1115 ◽

2021 ◽

Vol 60 (4) ◽

pp. 461-474

Author(s):

R. K. JENAMANI ◽

R. C. VASHISTH ◽

S. C. BHAN

Keyword(s):

Wind Speed ◽

Environmental Changes ◽

Maximum Wind Speed ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Peak Time ◽

New Delhi ◽

Maximum Wind ◽

Average Maximum

In the present study, commencement timings and duration of thunderstorms (TS) and squalls at IGI airport, Palam, New Delhi have been analysed critically based on most recent eleven years data of 1995-2005 to find their favourable time of occurrences. Then utility of such data base in the aviation warning has been demonstrated. Environmental changes associated with these squalls have also been further analysed to understand their impact. Being recent May 2007 a very cool month over Delhi, the role of TS on controlling the day’s soaring temperature has also been studied from their data. Results show TS are maximum in June followed by July whereas squalls are maximum in May followed by June. It shows more than 80% of TS in each season are of duration less than 3 hours with remaining are mostly 3 to 6 hours. The peak time period of commencement of both TS and squalls in the day differ with the progress of the months. For pre monsoon months, the most favourable timing of TS and squalls are 1200-1500 UTC while for monsoon, it starts earlier. Around 37% of the total TS during the period were associated with squalls. The average maximum wind speed in squall at IGI airport is about 68 kmph with highest maximum wind speed 139 kmph. On an average the environmental temperature falls by 5.6° C, humidity levels rises by 17.8% and mean sea level pressure rises by 1.6 hPa due to the occurrences of squalls. Study also shows daily maximum temperature rise is highly controlled by TS occurrences and May 2007, being a month of highest TS occurrences at the airport since 1995, became one of the coolest month in May over Delhi. The comparison of TS frequencies shows 12% increase in their annual activities since 1950-1980 with very high unusual increase of 51% in June and 26% in May. Since analysis of data from 1995 shows occurrences of TS are reversely but strongly correlated with summer temperatures and longer period temperature data since 1975 also confirms absence of significant trend in maximum temperature and higher temperature days in peak summer months of May and June till recent as expected due to high pollution, global warming and fast urbanization in the city, so it is the higher number of TS occurrences over the region from time to time which might have been main factor for controlling its significant rise.

Download Full-text

Coexistence without conflict, the recovery of Ireland’s endangered wild grey partridge Perdix perdix

European Journal of Wildlife Research ◽

10.1007/s10344-021-01470-w ◽

2021 ◽

Vol 67 (3) ◽

Author(s):

Kieran Buckley ◽

Conor O. Gorman ◽

Michael Martyn ◽

Brendan Kavanagh ◽

Alex Copland ◽

...

Keyword(s):

National Parks ◽

Survival Rates ◽

Maximum Temperature ◽

Peat Bogs ◽

Chick Survival ◽

Perdix Perdix ◽

Grey Partridge ◽

Average Maximum ◽

Average Maximum Temperature ◽

The Impact

AbstractBy 1995, Ireland’s wild grey partridge (Perdix perdix) was extinct nationally as a breeding species on farmland. The two populations remaining were confined to Ireland’s industrial cutaway peat bogs. One of these populations was deemed viable. In 1996, the National Parks and Wildlife Service of Ireland and the Irish Grey Partridge Conservation Trust established a conservation project to prevent the extirpation of this population. In this paper, we explore the impact of each management factor on two key demographic response variables: chick survival rates and the number of breeding pairs. The numbers of linear metres of nesting strips had the most significantly positive effect on spring pairs, followed by the total number of supplementary food hoppers and the total hectares of brood-rearing and over-winter cover. Counterintuitively, encounters with Hen Harriers (Circus cyaneus) did not negatively affect chick survival or the number of spring pairs. While we cannot rule out the contribution of each explanatory variable, none had a statistically significant effect on chick survival, suggesting there may be locally confounding factors that our model could not capture. The weather conditions during the peak hatching period had a significant influence on chick survival, with the average maximum temperature observed in June having the strongest positive association with an increase of 1 °C in the average maximum temperature in June associated with an increase in chick survival of 9.4% on average. Conversely, for every additional 1 mm of rain in June, there was a 0.23% drop in chick survival on average.

Download Full-text

Performance of RegCM4.5 in Simulating the Regional Climate of Western Tianshan Mountains in Xinjiang, China

Atmosphere ◽

10.3390/atmos12121544 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1544

Author(s):

Quanying Cheng ◽

Fan Li

Keyword(s):

Wind Speed ◽

Meteorological Factors ◽

Regional Climate ◽

Meteorological Factor ◽

Tianshan Mountains ◽

Maximum Temperature ◽

Simulation Performance ◽

Four Seasons ◽

Western Tianshan ◽

Average Maximum

The western Tianshan Mountains region in China has a complex topography where basins, mountains and glaciers co-exist. It is of great significance to study the sensitivity of meteorological factors in this region to different parameterization schemes of climate models. In this paper, the regional climate model RegCM4.5 is used to simulate the meteorological factor (mean temperature, maximum temperature, minimum temperature, precipitation and wind speed) occurring in the western Tianshan Mountains region from 2012 to 2016, so as to investigate the effects of different cumulus convective schemes (Grell, Tiedtke and Emanuel), including land cumulus convective schemes (LCCs) and ocean convective schemes (OCCs) on annual and seasonal simulations of meteorological factor by using the schemes of RUN1 (Grell for LCC and Tiedtke for OCC), RUN2 (Tiedtke for LCC and Emanuel for OCC), RUN3 (Grell for LCC and Emanuel for OCC) and ENS (the ensemble of RUN1, RUN2 and RUN3). The results show that the simulations of annual and seasonal meteorological factors are not significantly sensitive to the combination of LCCs and OCCs. In the annual simulations, RUN2 scheme has the best simulation performance for the maximum, average and minimum temperatures. However, other schemes of precipitation simulation outperform RUN2 scheme, and there is no difference among the four schemes for wind speed simulation. In the seasonal simulations, RUN2 scheme still performs well in the simulation of the average, maximum and minimum temperatures for four seasons, except for the simulation of the average temperature in spring and summer. For the simulation of the maximum temperature in summer, RUN2 scheme performs the same as ENS. For the simulation of other seasons, different meteorological factors have different performances in four seasons. Overall, the results show that different combinations of cumulus convection schemes can improve the simulation performance of meteorological factors in the western Tianshan Mountains of Xinjiang.

Download Full-text

Drought and households’ adaptive capacity to water scarcity in Kasali, Uganda

Journal of Water and Climate Change ◽

10.2166/wcc.2020.012 ◽

2020 ◽

Vol 11 (S1) ◽

pp. 217-232 ◽

Cited By ~ 1

Author(s):

Joseph Mukasa ◽

Lydia Olaka ◽

Mohammed Yahya Said

Keyword(s):

Adaptive Capacity ◽

Water Availability ◽

Water Scarcity ◽

Drought Index ◽

Annual Rainfall ◽

Maximum Temperature ◽

Seasonal Temperature ◽

Average Maximum ◽

June July ◽

The Impact

Abstract The world is experiencing variability in precipitation, increased temperature, drought frequencies and intensities. Globally, approximately four billion individuals experience water scarcity due to drought. In Uganda about 10% of the population in the southern and northern parts of the country experience drought related water scarcity annually. This study aimed at assessing drought and households’ adaptive capacity (AC) to water scarcity during drought in Kasali. This was done through determining drought trends from 1987 to 2017, assessing the impact of drought on water availability and the AC of households to manage water scarcity. Droughts were assessed based on the Reconnaissance Drought Index (RDI). The results show a decrease in the average annual rainfall, and the seasons of March-April-May (MAM), January-February (JF) while the seasons of September-October-November-December (SOND) and June-July-August (JJA) show an increase in rainfall trend. The average maximum and minimum annual and seasonal temperature increased significantly by between 0.56 and 1.51 °C. The minimum temperature increased more than the maximum temperature. Kasali experienced one extreme dry year and four moderate ones between 1987 and 2017. Above 70% of the households spend longer hours collecting water during dry years than wet years. The AC of households to water scarcity was low and drought negatively impacted water availability.

Download Full-text