This study measured Ambient concentrations of air pollutants and Volatile Organic Compounds (VOCs) in industrial park in Kaohsiung City. The spatial distribution was investigated during different time periods and seasons. The ozone formation potential (OFP) of VOCs species were evaluated based on the maximum incremental reactivity (MIR). Also, this study using factor analysis to estimate the polluted source. The season distribution of air pollutants showed concentration in spring higher than summer, owing to air activities of summer are acute include wet precipitation, photochemical reaction, and convection. The time period distribution showed the results which NOx and O3 concentration occurred peaks at 7:00 − 8:00, 18:00 − 19:00 and 13:00 − 16:00, respectively. The reason is photochemical reaction, lead to concentration trend with time of NOx inversely to O3. The concentration trend with time of CO and PM10 similar to NOx. The polluted sources were estimated mobile. By the way, O3 is proportional to temperature, but it is Inversely proportional to humility. The seasons distribution of VOCs showed most abundant species included 2-butanone, toluene, and n-pentane in spring, while included toluene, acetone, m,p-xylene, and methyl methacrylate in summer. According to percent composition, most abundant categories in spring and summer were both aromatics, ketones, and alkanes. The TVOC concentration was spring (164.6 £gg/m3) higher than summer (116.4 £gg/m3). The time periods distribution of VOCs showed most abundant categories included aromatics and ketones in morning and evening, while included aromatics and alkanes in night. The TVOC concentration of evening (163.2 ¡Ó 62.7 £gg/m3) was highest, followed by night (159.9 ¡Ó 87.4 £gg/m3), Lowest was morning (98.4 ¡Ó 32.3 £gg/m3). Results showed alkanes and alkenes own higher concentration in night, ketones and esters in evening, and aromatics in evening and night. The reason is related with sunshine, inversion layer, and lower wind speed. By the way, TVOC is proportional to temperature. In spring, the OFP was 566.0 £gg-O3/m3, OFP/TVOC was 3.44. In summer the OFP was 629.3 £gg-O3/m3, OFP/TVOC was 5.41. It was worth mentioning highest OFP categories in spring and summer was both aromatics (332.2 £gg-O3/m3, 380.3 £gg-O3/m3), and highest OFP species was toluene (138.8 £gg-O3/m3) and methyl methacrylate (171.7 £gg-O3/m3) , respectively. The results from factor analyses showed the predominant source included mobile polluted source, petrol evaporation, related electronic industry, metallurgy industry, refinery, and architectural coatings escape in spring. The predominant source included mobile polluted source, petrol evaporation, plastic industry, steel industry, and related electronic industry in summer.

Submitted by Elcio Hissagy Samecima Júnior (elciosamecimajr@hotmail.com) on 2018-04-16T17:43:05Z No. of bitstreams: 1 Dissertação_Elcio_Hissagy_Samecima_Junior.pdf: 1068541 bytes, checksum: 75dddd77d07a71a9442232dc1cc0fcde (MD5) / Approved for entry into archive by Alexandra Maria Donadon Lusser Segali null (alexmar@fcav.unesp.br) on 2018-04-16T18:28:37Z (GMT) No. of bitstreams: 1 samecimajunior_eh_me_jabo.pdf: 1068541 bytes, checksum: 75dddd77d07a71a9442232dc1cc0fcde (MD5) / Made available in DSpace on 2018-04-16T18:28:37Z (GMT). No. of bitstreams: 1 samecimajunior_eh_me_jabo.pdf: 1068541 bytes, checksum: 75dddd77d07a71a9442232dc1cc0fcde (MD5) Previous issue date: 2018-02-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / RESUMO – O melhoramento vegetal, além de buscar as características de interesse, busca também otimizar o processo. Sendo assim, quando há correlação entre as características de interesse e uma de fácil avaliação, abre-se a vertente para a seleção indireta. A utilização de sensores na agricultura possibilita a avaliação sem contato físico, podendo ser uma nova ferramenta na seleção indireta, visando otimizar tempo, mão de obra, custo e o processo. Objetivou-se estudar a relação entre o Índice de Vegetação por Diferença Normalizada (NDVI) e os caracteres agronômicos, na seleção indireta em milho e selecionar os genótipos superiores utilizando técnicas multivariadas. O experimento foi conduzido na segunda safra de 2016, sendo realizadas as medições de NDVI via sensor ativo terrestre, a cada 15 dias após a emergência das plântulas e as avaliações agronômicas de campo considerando os caracteres: altura de planta, altura da espiga principal, acamamento, quebramento, estande e produtividade. O conjunto de variáveis obtidas foram submetidas as análises multivariadas de fatores e de componentes principais. A análise de fatores detectou, no primeiro fator, correspondências positivas entre as variáveis, altura de planta, altura de espiga e produtividade, no segundo fator NDVI-80, NDVI-95 e acamamento mais quebramento e no terceiro fator NDVI-15 e estande. Os gráficos biplots gerados pelos componentes principais, juntamente com análise de ganho de seleção permitiram identificar o genótipo 3 como o mais promissor, por apresentar baixo acamamento mais quebramento, baixo valor de NDVI-80 e NDVI-95 e alta produtividade em relação a este experimento. A partir de todos os resultados concluise que é possível usar o NDVI, via sensor ativo terrestre, como técnica promissora na seleção indireta, para acamamento, quebramento e predição do estande em milho. / ABSTRACT - The plant breeding look beyond the characteristics of interest, look to optimize process, so when there is a correlation between the characteristics of interest and one of easy evaluation, a strand is opened for indirect selection. The use of sensors in agriculture makes possible to evaluate without physical contact and it can be a new tool in the indirect selection, aiming to optimize time, work, cost and optimize process. The objective of this study was to analyse the relationship between Normalized Difference Vegetation Index (NDVI) and agronomic traits, in the indirect selection and to select superior genotypes of maize by multivariate analyse. The experiment was conducted in the second crop of 2016, and NDVI was measurements by an active sensor every 15 day after seedling and field agronomic traits were evaluated considering the following characteristics: plant height, ear height, stalk lodging, stalk breakage, stand and yield. With these data were processed the factor analyzes, principal component and gain selection. The factor analysis detected positive correspondences between the variables, plant height, ear height and yield with factor 1; NDVI-80, NDVI-95 and stalk lodging plus stalk breakage with factor 2; NDVI- 15 and stand with factor 3. The graphics biplots generated by the principal components with gain selection analyze allowed to identify the best genotype, where we could identify the genotype 3 as the most promising, because it present lower lodging plus breakage stalk, low value of NDVI-80 and 95, and high yield in relation to this experiment. From all results it is concluded that it is possible to use the NDVI, by active sensor, as a promising technique in the indirect selection, for stalk lodging, breakage and stand in maize.