Weather variables

The following work describes the control method developed on a greenhouse destined to the cultivation of 4 different varieties of chrysanthemums. There is also included a detail report from the control method applied to each one of the weather variables, such as temperature, relative humidity, luminosity and the measurement of CO 2 , all of which affect the growing process of the flowers inside the greenhouse.

The following work describes the control method developed on a greenhouse destined to the cultivation of 4 different varieties of chrysanthemums. There is also included a detail report from the control method applied to each one of the weather variables, such as temperature, relative humidity, luminosity and the measurement of CO 2 , all of which affect the growing process of the flowers inside the greenhouse.

Poverty remains one of the key development goals for developing countries. Achieving this goals may be far-fetched given the increasing vagaries from climate change. This paper sought to estimate the effect of climate and weather variability on chronic poverty using household panel data for Kenya. Using Chamberlain random effects probit model with control function, the paper found that weather variability reduces the likelihood of a household falling into chronic poverty. Household heads with post-secondary education were less likely to fall into chronic poverty and those who earned off-farm income and had access to credit facilities had lower probability of falling into poverty as compared to those who did not. The findings suggests the need to devise policies on climate mitigation in order to cushion households from devastating effects of weather variability and to create awareness through dissemination of climate information in easy and widely accessible formats.

The aim behind doing this research is to analyse the impact of odd-even policy and lockdown implementation on the air quality index of Delhi by doing the case study on the four regions Ashok Vihar, Anand Vihar, Dwarka and R.K. Puram. The data is been collected from DPCC and the main parameters we looked for are PM10 and PM2.5. In which we find out that highest levels of the pollutants PM10 and PM2.5 been observed during the time of odd-even policy implementation for the year 2019 (04 November 2019-15 November 2019) whereas during the lockdown period (23 March 2020-31st August 2020) a great decline in pollutant levels is been detected. This we further try to correlate with the spatial variations of Delhi region and able to discern that meteorological parameters (Ambient Temperature, Relative Humidity, Wind Speed and Solar Radiations) in respect with seasonal variations have a major influence on PM 10 and PM 2.5 levels. During the winter season both the parameters PM10 & PM2.5 are touching the peak because of the impact of three major meteorological parameters Ambient Temperature, Wind Speed and Solar Radiation and during the monsoon season air quality conditions are quite favourable because of Ambient Temperature and Wind Speed parameters. In the end we use the ensembled machine learning algorithms like Random Forest and Extra trees regressor to have an accurate estimation of PM2.5 levels for all the four regions of Delhi and perceived that these ensembled learning techniques are better than other machine learning algorithms like Neural Networks, Linear regression and SVMs. The Random Forest and Extra trees regressor models give the R 2 value 0.75 and 0.78 respectively for estimation of PM2.5; R 2 value is a statistical measurement which explains the variance of dependent variable based on the independent variables of a regression model.