title: Ð¡ÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð· ÐºÐ°ÑÐµÑÑÐ²Ð° Ð²Ð¾Ð´Ñ Ð½Ð° Ð²Ð¾Ð´Ð¾Ð·Ð°Ð±Ð¾ÑÐ°Ñ ÐÐµÐ²Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Statistical analysis of water quality at the Neva intakes
Creators: name: Ð¡Ð¸Ð¼Ð¾Ð½Ð¾Ð², ÐÐ½Ð°ÑÐ¾Ð»Ð¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: ÐÐµÐ²Ð°
ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð²Ð¾Ð´Ñ
Ð²Ð¾Ð´Ð¾Ð·Ð°Ð±Ð¾ÑÑ
Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð³Ð»Ð°Ð²Ð½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½Ñ
Neva river
water quality
intakes
principal component analises

description: Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ Ð·Ð°ÐºÐ»ÑÑÐ°Ð»Ð°ÑÑ Ð² ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¼ Ð°Ð½Ð°Ð»Ð¸Ð·e Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð²Ð¾Ð´Ñ ÑÐµÐºÐ¸ ÐÐµÐ²Ñ Ð½Ð° Ð²Ð¾Ð´Ð¾Ð·Ð°Ð±Ð¾ÑÐ°Ñ Ð´Ð²ÑÑ Ð²Ð¾Ð´Ð¾Ð¿ÑÐ¾Ð²Ð¾Ð´Ð½ÑÑ ÑÑÐ°Ð½ÑÐ¸Ð¹ (ÐÐ¡) Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³Ð°. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð»Ð¸ÑÐµÑÐ°ÑÑÑÐ½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ Ð¾ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð°Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÐºÐ°ÑÐµÑÑÐ²Ð° Ð²Ð¾Ð´Ñ. 2. ÐÐ¿ÑÐµÐ´ÐµÐ»Ð¸ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð±Ð°Ð·Ð¾Ð²Ð¾Ð¹ Ð¾Ð¿Ð¸ÑÐ°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð´Ð»Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÐµÐ¹ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð½ÐµÐ²ÑÐºÐ¾Ð¹ Ð²Ð¾Ð´Ñ Ð½Ð° Ð¡ÐµÐ²ÐµÑÐ½Ð¾Ð¹ Ð¸ ÐÐ»Ð°Ð²Ð½Ð¾Ð¹ ÐÐ¡. 3. ÐÑÑÐ²Ð¸ÑÑ ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¸ Ð¼ÐµÐ¶Ð´Ñ Ð·Ð½Ð°ÑÐµÐ½Ð¸ÑÐ¼Ð¸ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÐµÐ¹ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð²Ð¾Ð´Ñ. 4. ÐÐµÑÐ¾Ð´Ð¾Ð¼ Ð³Ð»Ð°Ð²Ð½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½Ñ Ð²ÑÑÐ²Ð¸ÑÑ ÑÐ°ÐºÑÐ¾ÑÑ, Ð²Ð»Ð¸ÑÑÑÐ¸Ðµ Ð½Ð° ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð²Ð¾Ð´Ñ, Ð¾ÑÐµÐ½Ð¸ÑÑ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ Ð¸ÑÑÐ¾ÑÐ½Ð¸ÐºÐ¸ Ð·Ð°Ð³ÑÑÐ·Ð½ÐµÐ½Ð¸Ñ Ð²Ð¾Ð´Ñ ÐÐ¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° 240-350 Ð¿ÑÐ¾Ð± Ð¿Ð¾ 13 Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÑÐ¼, Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ Ð¿Ð¾ Ð±Ð¾Ð»ÑÑÐ¸Ð½ÑÑÐ²Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÐµÐ¹ Ð² ÑÐµÑÐµÐ½Ð¸Ðµ 2011 Ð³Ð¾Ð´Ð° Ð¿ÑÐµÐ²ÑÑÐ°Ð»Ð¸ Ð½Ð¾ÑÐ¼Ð°ÑÐ¸Ð²Ñ. ÐÐ¾Ð»Ð¾Ð¶Ð¸ÑÐµÐ»ÑÐ½Ð°Ñ ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð½Ð° Ð¼ÐµÐ¶Ð´Ñ ÑÐ²ÐµÑÐ½Ð¾ÑÑÑÑ Ð¸ Ð¿ÐµÑÐ¼Ð°Ð½Ð³Ð°Ð½Ð°ÑÐ½Ð¾Ð¹ Ð¾ÐºÐ¸ÑÐ»ÑÐµÐ¼Ð¾ÑÑÑÑ, Ð¼ÑÑÐ½Ð¾ÑÑÑÑ Ð¸ ÑÐ¾Ð´ÐµÑÐ¶Ð°Ð½Ð¸ÐµÐ¼ Ð°Ð»ÑÐ¼Ð¸Ð½Ð¸Ñ, Ð¼ÐµÐ¶Ð´Ñ Ð½ÐµÐºÐ¾ÑÐ¾ÑÑÐ¼ Ð¼Ð¸ÐºÑÐ¾Ð±Ð¸Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ð¼Ð¸ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÑÐ¼Ð¸. Ð¢ÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÐ° Ð¾ÑÑÐ¸ÑÐ°ÑÐµÐ»ÑÐ½Ð¾ ÐºÐ¾ÑÑÐµÐ»Ð¸ÑÑÐµÑ Ñ ÑÐ²ÐµÑÐ½Ð¾ÑÑÑÑ, Ð¿ÐµÑÐ¼Ð°Ð½Ð³Ð°Ð½Ð°ÑÐ½Ð¾Ð¹ Ð¾ÐºÐ¸ÑÐ»ÑÐµÐ¼Ð¾ÑÑÑÑ, Ð½ÐµÐºÐ¾ÑÐ¾ÑÑÐ¼Ð¸ Ð¼Ð¸ÐºÑÐ¾Ð±Ð¸Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ð¼Ð¸ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÑÐ¼Ð¸. ÐÐ½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð³Ð»Ð°Ð²Ð½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½Ñ (ÐÐ) Ð²ÑÑÐ²Ð¸Ð» ÑÐµÑÑÑÐµ Ð·Ð½Ð°ÑÐ¸Ð¼ÑÑ ÐÐ, Ð¾Ð±ÑÑÑÐ½ÑÑÑÐ¸Ñ 70% Ð´Ð¸ÑÐ¿ÐµÑÑÐ¸Ð¸. ÐÑÐµÐ´Ð¿Ð¾Ð»Ð°Ð³Ð°ÐµÑÑÑ, ÑÑÐ¾ Ð¿ÐµÑÐ²ÑÐ¹ ÐÐ ÑÐ²ÑÐ·Ð°Ð½ Ñ Ð¿Ð°Ð²Ð¾Ð´ÐºÐ°Ð¼Ð¸, Ð²ÑÐ¾ÑÐ¾Ð¹ â Ñ ÑÐ°ÑÑÐ¾Ð´Ð¾Ð¼ ÐÐµÐ²Ñ, ÑÑÐµÑÐ¸Ð¹ - Ñ Ð°Ð½ÑÑÐ¾Ð¿Ð¾Ð³ÐµÐ½Ð½ÑÐ¼ Ð¼Ð¸ÐºÑÐ¾Ð±Ð½ÑÐ¼ Ð·Ð°Ð³ÑÑÐ·Ð½ÐµÐ½Ð¸ÐµÐ¼ Ð² ÑÐ¾ÑÑÐ°Ð²Ðµ Ð²Ð·Ð²ÐµÑÐµÐ¹.
description: The work includes statistical analysis of raw water parameters in the Neva at two waterworks in St. Petersburg. The research set the following goals: 1. To analyze the literature on statistical methods for analyzing water quality. 2. Determine the parameters of the basic descriptive statistics (minimum, maximum, average values) for the quality indicators of Neva water at the Northern and Main water supply stations. 3. To identify correlations between the values of water quality indicators. 4. Using the principal component analysis, identify factors affecting water quality, evaluate the main sources of water pollution Statistical analysis was performed based on the results of 240-350 measurements obtained during the 2011 year on 13 indicators. The values of most parameters exceeded the established norms. A positive correlation is shown between color and permanganate index, turbidity and aluminum content, between some microbiological indicators (for example, the concentration of total coliform bacteria and thermotolerant coliform bacteria). Temperature negatively correlated with the color, permanganate index, and some microbiological indicators. Principal component analysis (PCA) revealed four significant major PCs explaining 70% of the variance. It is assumed that the first main component is the seasonal variability of water flow in the Neva, the second â with the Neva flow rate, and the remaining components are associated with pollution sources.