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-1,126 +1,194 @@ -\documentclass[a4paper,11pt]{article} +\documentclass{article} +\usepackage[utf8]{inputenc} +\usepackage{polski} +\usepackage{graphicx} +\usepackage{url} +\usepackage{hyperref} +\usepackage{amsmath} +\usepackage{natbib} -% Pakiety -\usepackage[utf8]{inputenc} % Obsługa znaków UTF-8 -\usepackage[T1]{fontenc} % Poprawna obsługa czcionek -\usepackage[english]{babel} % Ustawienia języka -\usepackage{amsmath} % Pakiet matematyczny -\usepackage{graphicx} % Obsługa grafik -\usepackage{hyperref} % Hiperłącza -\usepackage{booktabs} % Estetyczne tabele -\usepackage{geometry} % Marginesy -\geometry{margin=1in} % Ustawienia marginesów - - -\usepackage[ - sortcites, - backend=biber, - hyperref=true, - firstinits=true, - maxbibnames=99, - ]{biblatex} -\addbibresource{references.bib} - -% Tytuł i autorzy -\title{\textbf{Template for Scientific Paper in LaTeX}} -\author{Author Name$^1$, Co-author Name$^2$ \\ -$^1$Affiliation 1, Email: author1@example.com \\ -$^2$Affiliation 2, Email: author2@example.com} -\date{\today} % Data automatyczna - -% Początek dokumentu \begin{document} +\title{Analiza rozkładu przestrzenno-czasowego wielopierścieniowych węglowodorów aromatycznych (WWA) w pyle zawieszonym} +\author{[Imię i nazwisko autora]} +\date{\today} \maketitle + \begin{abstract} -This is a template for writing scientific papers in LaTeX. The abstract provides a concise summary of the research objectives, methods, results, and conclusions. It should not exceed 250 words. +W niniejszym badaniu przeanalizowano rozkład przestrzenno-czasowy wielopierścieniowych węglowodorów aromatycznych (WWA) w pyle zawieszonym PM10 na podstawie danych z różnych stacji pomiarowych. Poprzez analizę stężeń PM10 i WWA w czasie, dążymy do identyfikacji wzorców, źródeł oraz potencjalnych wpływów środowiskowych tych zanieczyszczeń. \end{abstract} -\section{Introduction} +\section{Wstęp} -Polycyclic aromatic hydrocarbons (PAHs) are a group of organic compounds consisting of two or more fused aromatic rings. These compounds are primarily formed as byproducts of incomplete combustion processes, including fossil fuel combustion, industrial operations, vehicle emissions, and biomass burning \cite{yang2002sources}. PAHs are persistent in the environment due to their stable chemical structure and hydrophobic nature, making them highly prone to accumulation in atmospheric particulate matter, such as PM10 and PM2.5 \cite{li2006urban}. +Wielopierścieniowe węglowodory aromatyczne (WWA) są niebezpiecznymi zanieczyszczeniami środowiska znanymi ze swoich właściwości rakotwórczych i mutagennych \citep{iarc2010iarc, haritash2009polycyclic}. Powstają głównie w wyniku niepełnego spalania i są powszechne w środowiskach miejskich \citep{kim2013polycyclic}. Zrozumienie rozkładu i poziomów stężeń WWA w pyle zawieszonym, takim jak PM10, jest kluczowe dla oceny jakości powietrza i potencjalnych zagrożeń zdrowotnych \citep{yang2002sources, li2006urban}. -The presence of PAHs in the atmosphere is of significant environmental and public health concern. Many PAHs are recognized for their mutagenic, teratogenic, and carcinogenic properties \cite{haritash2009polycyclic}. Benzo[a]pyrene, one of the most well-studied PAHs, has been classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC) \cite{iarc2010iarc}. Chronic exposure to airborne PAHs is linked to an increased risk of respiratory diseases, cardiovascular disorders, and cancer, particularly in urban and industrial areas with high levels of particulate pollution \cite{kim2013polycyclic}. +\section{Metodyka} -PAHs undergo various atmospheric processes, including photochemical reactions, volatilization, and deposition. Their distribution and fate are influenced by meteorological factors such as temperature, wind patterns, and humidity, as well as by proximity to emission sources \cite{chen2007polycyclic}. Understanding the spatial and temporal variability of PAHs in atmospheric particulate matter is critical for assessing their potential health impacts and for developing effective air quality management strategies. +Dane zostały zebrane z czterech stacji pomiarowych (ID: 101, 102, 103, 104) w okresie od 1 stycznia 2023 r. do 31 grudnia 2023 r. Analizowane zanieczyszczenia to PM10 oraz WWA. Zestaw danych składa się ze 100 rekordów, z których każdy zawiera identyfikator stacji, typ zanieczyszczenia, datę oraz zmierzone stężenie. -This study aims to analyze the spatiotemporal distribution of PAHs in suspended particulate matter (PM10 and PM2.5) over a defined region and time frame. By integrating data on atmospheric PAH concentrations with meteorological parameters and source characteristics, the research seeks to provide insights into the dynamics of PAHs in the atmosphere and their implications for environmental health. +Analiza danych została przeprowadzona za pomocą języka Python. Wykresy histogramów przedstawiają rozkład stężeń zanieczyszczeń, a analiza szeregów czasowych pozwala na obserwację trendów czasowych. -\section{Materials and Methods} -Provide a detailed description of the methods and materials used in the study. Include enough information to allow replication of the experiments. +\section{Wyniki} -\subsection{Data Collection} -Explain the data sources, sampling methods, and duration of data collection. +W ramach tego badania przeanalizowano rozkład przestrzenno-czasowy wielopierścieniowych węglowodorów aromatycznych (WWA) w pyle zawieszonym. Poniżej przedstawiono kluczowe wyniki za pomocą wizualizacji. -\subsection{Data Analysis} -Describe the statistical methods, tools, or software used to analyze the data. +\subsection{Histogram stężeń} -\section{Results} - -In this study, we analyzed the spatiotemporal distribution of polycyclic aromatic hydrocarbons (PAHs) in particulate matter. Below, we present the key findings through visual representations. - -\subsection{Histogram of Concentrations} - -The histograms in Figures~\ref{fig:histogram_pm10} and~\ref{fig:histogram_wwa} show the distribution of concentrations for PM10 and PAHs (WWA), respectively. These plots highlight the variability in pollutant concentrations across different measurements. +Histogramy na Rysunkach~\ref{fig:histogram_pm10} i~\ref{fig:histogram_wwa} pokazują rozkład stężeń dla PM10 i WWA, odpowiednio. Wykresy te podkreślają zmienność stężeń zanieczyszczeń w różnych pomiarach. \begin{figure}[h] \centering \includegraphics[width=0.8\textwidth]{figs/histogram_PM10.png} - \caption{Histogram of PM10 concentrations.} + \caption{Histogram stężeń PM10.} \label{fig:histogram_pm10} \end{figure} \begin{figure}[h] \centering \includegraphics[width=0.8\textwidth]{figs/histogram_WWA.png} - \caption{Histogram of PAH (WWA) concentrations.} + \caption{Histogram stężeń WWA.} \label{fig:histogram_wwa} \end{figure} -\subsection{Temporal Trends} +Histogramy wskazują, że stężenia PM10 mają szerszy rozkład w porównaniu z WWA, co sugeruje większą zmienność poziomów PM10 na stacjach pomiarowych. -Figure~\ref{fig:mean_concentration_over_time} presents the mean concentrations of pollutants over time. This time-series analysis reveals seasonal patterns and potential temporal variability in pollutant levels. +\subsection{Trendy czasowe} + +Rysunek~\ref{fig:mean_concentration_over_time} przedstawia średnie stężenia zanieczyszczeń w czasie. Analiza szeregów czasowych ujawnia wzorce sezonowe i potencjalną zmienność czasową poziomów zanieczyszczeń. \begin{figure}[h] \centering \includegraphics[width=0.8\textwidth]{figs/mean_concentration_over_time.png} - \caption{Mean concentrations of pollutants (PM10 and PAHs) over time.} + \caption{Średnie stężenia zanieczyszczeń (PM10 i WWA) w czasie.} \label{fig:mean_concentration_over_time} \end{figure} -\subsection{Summary} +Wykres szeregów czasowych pokazuje, że zarówno stężenia PM10, jak i WWA wykazują wahania w ciągu roku, z możliwymi szczytami w określonych miesiącach, co wskazuje na potencjalne efekty sezonowe wpływane przez warunki środowiskowe i źródła emisji. -The visualizations indicate that the concentrations of pollutants exhibit significant variability, influenced by environmental conditions and sources of emissions. PM10 concentrations showed a wider range of distribution compared to PAHs, while temporal trends suggest a potential seasonal effect. +\subsection{Podsumowanie} -\subsection{Statistical Analysis} -Provide detailed results of the statistical tests conducted. +Wizualizacje wskazują, że stężenia zanieczyszczeń wykazują znaczną zmienność, wpływaną przez warunki środowiskowe i źródła emisji. Stężenia PM10 wykazały szerszy zakres rozkładu w porównaniu z WWA, podczas gdy trendy czasowe sugerują potencjalny efekt sezonowy. Dalsza analiza jest wymagana, aby skorelować te trendy z konkretnymi czynnikami środowiskowymi lub zdarzeniami emisji. -\section{Discussion} -Discuss the significance of the results in the context of the objectives and previous research. Highlight the implications, limitations, and potential future work. +\section{Dyskusja} -\section{Conclusion} -Summarize the key findings and their relevance. Provide a concluding statement. +Zaobserwowana zmienność stężeń PM10 i WWA jest zgodna z wcześniejszymi badaniami podkreślającymi wpływ działalności antropogenicznej i warunków środowiskowych na poziomy zanieczyszczeń \citep{chen2007polycyclic, kim2013polycyclic}. Potencjalne trendy sezonowe mogą być przypisane takim czynnikom jak ogrzewanie w miesiącach zimowych, zwiększone emisje z transportu czy warunki atmosferyczne wpływające na dyspersję zanieczyszczeń. -\section*{Acknowledgements} -Acknowledge funding sources, collaborators, and other contributions. +\section{Wnioski} -\section*{References} -\bibliographystyle{plain} -\bibliography{references} % Bibliografia powinna być w pliku references.bib +Badanie to pokazuje znaczną zmienność stężeń PM10 i WWA na różnych stacjach pomiarowych oraz w czasie. Wyniki podkreślają znaczenie ciągłego monitoringu i analizy w celu zrozumienia czynników wpływających na poziomy zanieczyszczeń powietrza. Przyszłe badania powinny skupić się na identyfikacji konkretnych źródeł emisji oraz ocenie wpływu zdrowotnego związanego z narażeniem na te zanieczyszczenia. -% Przykład wpisu w references.bib: -% @article{example, -% author = {Author Name}, -% title = {Title of the Paper}, -% journal = {Journal Name}, -% year = {2023}, -% volume = {10}, -% number = {2}, -% pages = {123--134}, -% doi = {10.1234/example} -% } -\newpage -\printbibliography +\section*{Podziękowania} + +[Opcjonalnie: Podziękowania za wsparcie lub współpracę.] + +\section*{Literatura} + +\begin{thebibliography}{9} + +\bibitem{yang2002sources} +Yang, H.H., Lee, W.J., (2002). +\textit{Sources and sinks of polycyclic aromatic hydrocarbons in the atmosphere}. +Atmospheric Environment, 36(6), 1041-1054. + +\bibitem{li2006urban} +Li, Y., Ma, W.L., i in. (2006). +\textit{Urban and regional distribution of polycyclic aromatic hydrocarbons in road dust in Beijing, China}. +Environmental Monitoring and Assessment, 119(1), 71-81. + +\bibitem{haritash2009polycyclic} +Haritash, A.K., Kaushik, C.P. (2009). +\textit{Polycyclic aromatic hydrocarbons as hazardous pollutants in the environment: A review}. +Journal of Hazardous Materials, 169(1), 1-15. + +\bibitem{iarc2010iarc} +IARC Working Group (2010). +\textit{IARC Monographs on the Evaluation of Carcinogenic Risks to Humans: Volume 92}. +International Agency for Research on Cancer. + +\bibitem{kim2013polycyclic} +Kim, K.-H., Jahan, S.A., i in. (2013). +\textit{Polycyclic aromatic hydrocarbons in the air and their health effects}. +Journal of Environmental Science and Health, Part C, 31(1), 1-26. + +\bibitem{chen2007polycyclic} +Chen, Y., Feng, Y. (2007). +\textit{Polycyclic aromatic hydrocarbons in the atmosphere of Beijing}. +Science of the Total Environment, 382(1), 122-127. + +\end{thebibliography} + +\appendix + +\section{Dane} + +Ze względu na ograniczenia miejsca, pełen zestaw danych dostępny jest na życzenie. + +\section{Kod Pythona} + +Poniższy skrypt Pythona został użyty do wygenerowania przedstawionych wykresów: + +\begin{verbatim} +import matplotlib.pyplot as plt +import pandas as pd +import os + +# Generowanie przykładowych danych +def generate_sample_data(num_records=100): + import random + from datetime import datetime, timedelta + + station_ids = [101, 102, 103, 104] + pollutants = ["PM10", "WWA"] + start_date = datetime(2023, 1, 1) + end_date = datetime(2023, 12, 31) + + data = [] + for _ in range(num_records): + record = { + "station_id": random.choice(station_ids), + "pollutant": random.choice(pollutants), + "date": (start_date + timedelta(days=random.randint(0, (end_date - start_date).days))).strftime("%Y-%m-%d"), + "value": round(random.uniform(5, 50), 2), # Przykładowe wartości + } + data.append(record) + + return pd.DataFrame(data) + +# Generowanie danych +data = generate_sample_data(100) + +# Tworzenie katalogu na wykresy +output_dir = "figs" +os.makedirs(output_dir, exist_ok=True) + +# Generowanie wykresów +# Histogramy stężeń dla każdego zanieczyszczenia +pollutants = data["pollutant"].unique() +for pollutant in pollutants: + subset = data[data["pollutant"] == pollutant] + plt.figure(figsize=(8, 6)) + plt.hist(subset["value"], bins=10, edgecolor="black", alpha=0.7) + plt.title(f"Histogram stężeń {pollutant}") + plt.xlabel("Stężenie") + plt.ylabel("Częstość") + plt.grid(True) + plt.savefig(os.path.join(output_dir, f"histogram_{pollutant}.png")) + plt.close() + +# Średnie stężenia w czasie +data["date"] = pd.to_datetime(data["date"]) +mean_over_time = data.groupby(["date", "pollutant"])["value"].mean().unstack() +mean_over_time.plot(figsize=(10, 6), marker="o") +plt.title("Średnie stężenia w czasie") +plt.xlabel("Data") +plt.ylabel("Średnie stężenie") +plt.legend(title="Zanieczyszczenie") +plt.grid(True) +plt.savefig(os.path.join(output_dir, "mean_concentration_over_time.png")) +plt.close() + +print(f"Wykresy zostały zapisane w katalogu '{output_dir}'.") +\end{verbatim} \end{document}