Overall summary Environmental pollution is when our surroundings alter unfavourably, wholly or mainly as a byproduct of man's actions, through the effects of the changes in the energy pattern, radiation levels, and chemical and physical constitution and abundance of organisms, whether directly or indirectly. The most severe global challenge inextricably linked with rapid industrialization and urbanization is environmental pollution. Air, water, and land pollution are the three major types of environmental pollution in terms of global human health impacts. Other types of pollution considered to be human health threats are radioactive, thermal, light, and noise pollution. Scientific evidence for impacts on human health and well-being due to the various environmental pollution exposures is unequivocal. In recent years, the scientific community have made numerous attempts to estimate the effects of environmental pollution on the global burden of disease, either in terms of mortality or disability-adjusted life years (DALYs). The total disease burden which may be attributed to pollution is about 8-9%, but considerably more in poorer countries. Difficulties in unravelling associations between environmental pollution and health are created by the effects of cumulative exposures, long latency times, and multiple exposures to different pollutants, which might also act synergistically. The ambivalence inherent in the available data on mortality and morbidity, in existing knowledge about the aetiology of diseases, and in environmental information and estimates of exposure and the complexities involved in the link between environmental pollution and health, all mean that any attempt to assess the environmental contribution to the disease burden worldwide is swarmed with difficulties. The identification of the distribution of diseases, factors underlying their source and cause, and methods for their control makes epidemiology a science of high importance. The comprehension of how political, social, and scientific factors interact to exacerbate the risk of disease is a requirement, making epidemiology a unique science. Millions of lives have been saved through epidemiology, from both infectious and non-communicable diseases, through interventions and prevention programs implemented as a result of study findings. The Centers for Disease Control and Prevention (CDC) has estimated that medical epidemiologists added 25 years to the average life expectancy of the population of the United States of America since 1947. Currently, epidemiology continues to contribute to saving lives, although it has some limitations, such as the incompleteness of data and models and the inherent uncertainties. The most recent example is the role of epidemiology during the COVID-19 pandemic, which allowed the implementation of targeted and collaborative interventions to avoid outbreaks and reduce fatalities. Epidemiology can also play a significant role in increasing understanding of the impact of climate change on global disease burden. Along with a rise in inequality and urbanization, climate change presents new challenges for global health programs; in light of these, research in epidemiology is sure to remain a linchpin in guiding public health policies in the near future. The heuristics used to rank the relative strength of results obtained from scientific research is the hierarchy of evidence (or levels of evidence). There is an extensive agreement on the relative strength of large-scale epidemiological studies. The endpoints measured (such as survival or quality of life) and the study's design (such as a single case report for an individual patient or a randomized controlled trial) affect the strength of the evidence. In clinical research, meta-analyses of randomized controlled trials are the best evidence for treatment efficacy. Typically, systematic reviews of completed, high-quality randomized controlled trials – such as those published by the Cochrane Collaboration – rank as the highest quality of evidence above observational studies, while anecdotal experience and expert opinion are at the bottom level of evidence quality. In evidence-based practices, evidence hierarchies are often applied and are integral to evidence-based medicine (EBM). The burden of disease related to neurodevelopmental disorders, especially autism spectrum disorders (ASD) and attention deficit hyperactivity disorder (ADHD), is rapidly increasing in the last decades. Environmental exposure could play an important role in determining neurodevelopmental disorders in children, and various researchers have conducted epidemiological studies which show an association between prenatal and postnatal exposure and the development of the disease. However, there are no conclusive results, and further research is needed to throw light on this topic. Lead (Pb) represents one of the most dangerous pollutants, and it is classified by the World Health Organization as one of ten chemicals of major public health concern. Numerous child health issues are caused by lead exposure. Some of the effects include a lowered performance on intelligence tests, intellectual, behavioural, or motor function deficit, as well as hand-eye coordination and reaction problems. In addition, exposure to outdoor air pollution, particulate matter (PM) to be precise, appears to play an etiologic role on neurodevelopmental disorders, although the molecular mechanisms remain still unknown. Autism spectrum disorders (ASDs) is suspected to be linked to increased exposure to airborne particulate matter, and the association between particulate matter exposure and neurodevelopmental disorders in children was recently studied by several researchers. The objective of this thesis was to identify and review the current state of prior literature on the association between lead and particulate matter exposure and the incidence of attention deficit hyperactivity disorder (ADHD) in children. Moreover, we took the unparalleled opportunity given by lockdown measures implemented by Italy and Spain to improve our comprehension of how human activities contribute to air pollution in urban areas. The results were published in the following scientific contributions: 1. Donzelli, G., Carducci, A., Llopis-Gonzalez, A., Verani, M., Llopis-Morales, A., Cioni, L., & Morales-Suárez-Varela, M. (2019). The association between lead and attention-deficit/hyperactivity disorder: a systematic review. International journal of environmental research and public health, 16(3), 382. 2. Donzelli, G., Llopis-Gonzalez, A., Llopis-Morales, A., Cioni, L., & Morales- Suárez- Varela, M. (2020). Particulate matter exposure and attention- deficit/hyperactivity disorder in children: A systematic review of epidemiological studies. International journal of environmental research and public health, 17(1), 67. 3. Donzelli, G., Cioni, L., Cancellieri, M., Llopis Morales, A., & Morales Suárez- Varela, M. M. (2020). The Effect of the Covid-19 Lockdown on Air Quality in Three Italian Medium-Sized Cities. Atmosphere, 11(10), 1118. 4. Donzelli, G., Cioni, L., Cancellieri, M., Llopis-Morales, A., & Morales-Suárez- Varela, M. (2021). Relations between Air Quality and Covid-19 Lockdown Measures in Valencia, Spain. International Journal of Environmental Research and Public Health, 18(5), 2296. 5. Donzelli, G., Cioni, L., Cancellieri, M., Llopis-Morales, A., & Morales-Suárez- Varela, M. (2021). Air Quality during Covid-19 Lockdown. Encyclopedia, 1(3), 519-526. 6. Donzelli G., M. Morales-Suárez-Varela. Systematic review of the association between lead exposure and attention-deficit/hyperactivity disorder (ADHD). Il dialogo e la condivisione per la tutela della salute e dell’ambiente. Pisa, Polo Didattico Piagge, 21- 22/09/2018. 7. Donzelli G., A. Carducci, L. Cioni, M. Morales Suárez-Varela. L’associazione tra il piombo e il disturbo dell’iperattività. Una revisione sistematica della letteratura. Biomonitoraggio di ftalati e BPA nei bambini italiani e associazione con patologie infantili: il progetto europeo LIFE PERSUADED. 25-26/10/2018. Istituto Superiore di Sanità (Italian Institute of Health), Roma. 8. Donzelli G., A. Carducci, M. Verani, A. Llopis-Morales, I. Peraita-Costa, M. Morales- Suarez-Varela. The association between lead and attention- deficit/hyperactivity disorder. A systematic review. XV Congreso español de salud ambiental. 22- 24/05/2019, Valencia, Spain. 9. Donzelli G., L. Cioni, M. Cancellieri, A. Llopis Morales, M.M. Morales Suárez- Varela. Effect of Covid-19 lockdown measures on air quality in Valencia, Spain. XXXIX Reunión Científica SEE – XVI Congresso APE – XIX Congreso SESPAS. 7-10/09/2021, León, Spain. 10. Donzelli G, Baglietto L, Fusco P, Campani L, Nuvolone D, Ficorilli A, Malavasi G, De Marchi B, Tallacchini M, Biggeri A. Aria di Ricerca in Valle del Serchio. Ricerca partecipata in epidemiologia ambientale nell’ambito del progetto europeo H2020 “Cities_Health”. XLIII convegno AIE 2019, Epidemiologia: una, nessuna e centomila. Catania, 23-25/10/2019. 11. Deliverable CitieS-Health project. D3.1. Documentation on activities and outcomes in CS actions, first report. 31/12/2019 12. Deliverable CitieS-Health project. D4.3 Insights and recommendations for ethics and policy at the interface between academic and citizen science - Intermediate report. 30/06/2020 13. Deliverable CitieS-Health project. D3.2. Documentation on activities and outcomes in CS actions, second report. 31/12/2020 14. Biggeri A, De Marchi B, Donzelli G, Ficorilli A, Fusco P, Malavasi G, Doccioli C, Campani C, Amadei V, Angelini F, Andreuccetti P. Project" Aria di Ricerca in Valle del Serchio"(Tuscany Region, Central Italy): scenarios and implications. Epidemiologia e Prevenzione. 2021; 45 (1-2): 22-6. Main results The systematic review “The Association between Lead and Attention-Deficit/Hyperactivity Disorder: A Systematic Review” aimed to analyze the scientific literature for the potential relationships between lead exposure and ADHD to gain a deeper understanding of the effects of this pollutant on the mental health of children. EMBASE and MEDLINE (accessed from PubMed) were the databases on which the query was performed to pick out the publications eligible for inclusion in the review. The terms "environmental", "pollution", "lead" and "hyperactivity disorder” were used to conduct the literature searches in the following search string: ((“environmental” OR “pollution” OR “lead”) AND “hyperactivity disorder”)). A total number of 829 articles were identified, and 82 studies were left after the screening of titles and abstracts. To assess which articles had to be included in the systematic review, the 82 studies were downloaded in full text. The grading system proposed by the Scottish Intercollegiate Guidelines Network (SIGN) was used to establish levels of evidence and grades of recommendation. The study design and risk of bias are used to assess the quality of scientific evidence provided or the level of evidence according to what the SIGN scale of the level of evidence proposes. The numbers 1 to 4 are assigned to classify the level of evidence of the study design, while "++", "+" or "-" are assigned to represent the assessed risk of bias. The strength of the associated recommendations is categorized into "A", "B", "C", and "D" grades, in order from best to worst, based on this rating of the quality of the evidence in the articles. Included in this review are five cohort studies, ten case-control studies and two cross-sectional studies, which have been drawn from 9 different nations. Sample sizes varied from 117 to 2195, with a total of 8940 participants. The results showed that in 12 out of the 17 studies, a significant association was found between exposure to lead and one of the types of ADHD. The levels of lead in blood (BLLs) were determined in 14 of the 17 studies. Venipuncture was used to take blood samples from each child participant. In two of these studies, lead levels in the mothers' umbilical cord blood were also obtained and analyzed. Urine samples were obtained and analyzed by inductively coupled plasma mass spectrometry (ICP- MS) from one cohort and one case-control study. Another study gathered molar teeth that were sectioned longitudinally with a diamond blade on an Isomet low- speed saw (Buehler, Lake Bluff, IL, USA). One thing that should be considered is that 4 of the five studies that did not find any significant associations were classified as 2- in the scale used to evaluate the levels of evidence and, due to their high risk of bias, these should not be used when compiling recommendations. Furthermore, the other study which did not find any association considered the level of lead in urine samples. As urinary lead levels are less sensitive in the lower range of exposure, this fact can represent a bias. Overall, these findings have to be interpreted with caution because of the presence of high heterogeneity. Misdiagnosis could have led to heterogeneity in the results because of the occurrence of a case definition based on behaviour checklist fulfilled by parents or teachers (e.g., SNAP-IV) in the majority of the examined studies, rather than on medical diagnosis based on the Diagnostic and Statistical Manual of Mental Disorders. Moreover, the separation of hyperactivity-impulsivity symptoms and ADHD inattention was not considered by some studies. Also, the observation and time intervals of exposure used in the reviewed studies are not homogenous, and the rate of misdiagnosis could be notable in some studies since they were performed at an early age. Regarding the statistical methods, the connection between the risk of having ADHD and lead exposure was calculated using different approaches. The majority of studies used logistic regression models to obtain the adjusted odds ratios. However, different cut-off points derived from previous studies and the CDC guidelines were used to analyze data. Differently, some studies carried out the Spearman's Rank Correlation Coefficient and the Wilcoxon Rank Sum Test to analyze the relationship between ADHD and lead levels. Unfortunately, experimental studies such as randomized clinical trials are not readily feasible in environmental epidemiology, so confounding must always be considered when interpreting the causality of an association. The most frequent confounders considered in most of the studies examined in this review were age, maternal marital status, socioeconomic status, maternal smoking during pregnancy, educational years, sex, birth weight, children's age at behavioural testing paternal educational years. However, all studies reviewed did not account for the same potential confounding variables, which are a source of information bias in this review. In addition, five of the articles included in this review and classified with level 2- did not consider any confounding variables. To conclude, further research is needed to fully ascertain the nature of the connection between ADHD exposure to lead. The impact of the use of a standardized method of ADHD diagnosis and all potentially confounding variables should be considered in future studies. Also, they should be concentrated on the mother’s exposure to lead during late pregnancy and the early life of the children, and the combined exposure to multiple chemicals or risk factors should also be examined together with the effect of genetic factors. Since no systematic review had been done before, the "Particulate Exposure Disorder and Attention-Deficit/Hyperactivity Disorder in Children: A Systematic Review of Epidemiological Studies" was a necessary study to better understand the relationship between the two variables. The two databases EMBASE and Medline were searched for relevant documents following the instructions reported on the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) declaration. We only took into consideration studies looking at the association between PM exposure and ADHD disorders carried out on children of any age. Among Medical Subject Headings (MeSH) and non-MeSH keywords, PM, particulate matter, pollut*, ADHD, attention deficit, and hyperactivity were chosen, hence the following query was performed: (PM OR particulate matter OR pollut*) AND (“ADHD” OR “attention deficit” OR “hyperactivity disorder”). Only epidemiological investigations in English that studied the association between PM exposure and ADHD were included, whereas reviews, intervention studies, abstracts, letters to the editor, controlled studies, case reports, and in vitro and animal studies were not. Newcastle–Ottawa scale (NOS) was used to assess cohort studies quality about the following three aspects: election, comparability, and outcome. A final score assigned to each study could range from zero to nine stars, where zero to three stars indicated low quality, four to five stars indicated a satisfactory quality, six to seven indicated good quality, and eight to nine stars indicated a very good quality of the examined study. Studies had also been tested for the occurrence of any kind of bias through the use of the adapted Office of Health Assessment and Translation (OHAT) method developed by the National Institutes of Environmental Health Sciences National Toxicology Program and Navigation Guide, University of California. The most important biases tested for were those linked to the outcome and exposure assessment and confounding. Secondarily, selection-selective reporting-attrition/exclusion bias and others were assessed too. The synthetic evaluation was then given for each study ranging in four categories from low risk to high risk of bias. Once the selected query was launched on described databases, 774 papers were shown. After removing duplicates and looking at the titles and abstracts, only 24 resulted to be appropriate for the aim, and the text of these papers was hence fully evaluated. Inclusion criteria were matched only for twelve of these papers, ten cohort studies and two cross-sectional studies. This is the first systematic review aimed at examining the literature of epidemiological research on the possible connection between particulate matter exposure and the development of attention deficit hyperactivity disorder in children. Nine out of the twelve showed an association between particulate matter and the ADHD disorder, some even showed a dose-response relationship with higher prevalence/incidence of ADHD disorder for higher particulate matter levels. Unfortunately, the small number of studies conducted to probe this hypothesis till now and the high heterogeneity of those published cannot easily conduct strong evidence. The quality of the papers analyzed varied from "high" for five to "good" for the other seven articles. For both these reasons, the results of the present literature review have to be analyzed carefully, and more researchers' efforts worth be undertaken. It should be considered that various methods to assess attention deficit hyperactivity disorder were used. For example, some of the studies of this review used the Computerized Stroop test, while others used the Conners' Continuous Performance Test-II (CPT-II) and the Attention Network Test (ANT). Others added studies utilized the ADHD diagnosis based on the Diagnostic and Statistical Manual of mental disorders (DSM-IV) and extracted by National Health Insurance Services. This heterogeneity in the assessment of attention deficit hyperactivity disorder is a potential source of bias, and it makes difficult the comparison of the findings of the different studies. In addition, researchers selected different time windows of exposure. Bias due to misclassification of exposure could have been introduced by selecting different time windows in which exposure is defined. However, the selection of disparate time windows seems to be unrelated to the biological plausibility of ADHD development and outcomes. Also, different particulate matter diameters were considered as the source of exposure. Fine particulate matter, which poses a greater health risk as compared to the larger one, was considered only by some researchers and just one study evaluated particulate matter of nanoscale size (less than 0.1 μm). Furthermore, some studies applied Land Use Regression (LUR) models, while others utilized data from air quality monitoring networks. Finally, it should be considered that the different studies did not include in the analysis identical confounding factors, and for this reason, the comparison of results is not easy. For example, there is evidence that prenatal and postnatal exposure to 15 secondhand smoke (SHS) is linked with lowered intelligence and neurodevelopment disorders, however, secondhand smoke exposure at home was only included in the analysis as a confounder factor in some studies. Also, only two studies considered noise exposure as a confounder, although there is evidence that it is a significant risk factor in neurodevelopmental disorders. Although residual confounding will likely always be present, the design of epidemiological studies should include risk factors measurement for which we have scientific evidence. The objective of the study “Effect of the Covid-19 Lockdown on Air Quality in Three Italian Medium-Sized Cities” was to estimate the impact of the mobility restrictions imposed over the Covid-19 lockdown on the levels of air pollutants in Florence, Pisa, and Lucca. The level of air pollution was estimated through the measurement of the concentrations of particulate matter (PM2.5 and PM10), Ozone (O3), and Nitrogen Dioxide (NO2). More specifically, we collected the values of these air pollutants in three different periods, which are before, during, and after the lockdown period, comparing them with the values of the previous year. All cities included in this study have 'unsafe' levels of air pollution, and every year the daily limits prescribed by the World Health Organization (WHO) are exceeded. However, these cities are located in different geographical areas, and the population of them have different sizes, age structure, and mobility patterns. For example, the number of people doubles every day in Pisa due to the large number of students attending different universities and the tourists. It is important to recognize the characteristics of each city to correctly interpret the results of the analysis. To perform the analyses, the datasets containing officially estimates of the air pollutants available on the website of the ARPAT (Agenzia Regionale per la Protezione Ambientale della Toscana, the Regional Agency for the Environmental Protection of Tuscany) were accessed and reused. More specifically, the daily mean concentrations of particulate matter (PM2.5 and PM10), Ozone (O3), and Nitrogen Dioxide (NO2) were collected. Data from 1st January 2019 to 12th August 2020 were gathered and subsequently divided into two time periods of approximately eight months, which were called sampling periods. Practically, to perform the analyses, data from 1st January to 12th August 2020 were compared with the same period of the previous year. Each sampling period was divided into three time periods, which correspond with the pre-lockdown, lockdown and post-lockdown periods, respectively: - [01/01/2019 – 08/03/2019] versus [01/01/2020 – 08/03/2020] → pre- lockdown period; - [09/03/2019 – 03/06/2019] versus [09/03/2020 – 03/06/2020] → lockdown period; - [04/06/2019 – 12/08/2019] versus [04/06/2020 – 12/08/2020] → post- lockdown period. The results showed that the levels of air particulate matter did not significantly differ in the two years during the pre-lockdown period. All PM air concentrations registered by the air quality monitoring stations of the three cities showed no significant differences. The same results were observed when the PM concentrations of the lockdown period were compared with the previous year. The only exception is represented by the air quality monitoring station of Florence, in which a statistically significant reduction of 17 about 30.8% and 50.1% was observed for PM10 and PM2.5, respectively. In addition, an increase of the fine particulate matter (PM2.5) levels of about 33.3% was observed in 2020 in Pisa at an air quality station classified as a traffic station. Regarding the air quality levels of nitrogen dioxide (NO2), significant reductions were observed during the lockdown period in all the air-monitoring stations of the three investigated cities. Although these reductions are various in the different cities and areas, the findings show a stronger relationship between vehicular traffic and air concentrations of nitrogen dioxide. To conclude, air concentrations of ozone during the lockdown period was unchanged. Overall, these results could be very useful to design urban mobility plans for cities and improve the air quality. The objective of the study “Relations between Air Quality and Covid-19 Lockdown Measures in Valencia, Spain” was to estimate the impact of the mobility restrictions imposed over the Covid-19 lockdown on the levels of air pollutants in Valencia, Spain. The level of air pollution was estimated through the measurement of the concentrations of particulate matter (PM2.5 and PM10), Ozone (O3), and Nitrogen Oxides (NO2, NO and NOx). To perform the analyses, the datasets containing officially estimates of the air pollutants available on the website of the Generalitat Valenciana were accessed and reused. More specifically, the daily mean concentrations of particulate matter (PM2.5 and PM10), Ozone (O3), and Nitrogen Oxides (NO2, NO and NOx) were collected. Data from 1st January 2019 to 30th September 2020 were gathered and subsequently divided into two time periods of 9 months, which were call sampling periods. Practically, to perform the analyses, data from 1st January to 30th September 2020 were compared with the same period of the previous year. 18 Each sampling period was divided into six time periods, which correspond with the pre-lockdown (or normality 1), lockdown Phase 0, lockdown Phase 1, lockdown Phase 2, lockdown Phase 3, and post-lockdown (or normality 2) periods, respectively: - [01/01/2019 – 14/03/2019] versus [01/01/2020 – 14/03/2020] → pre- lockdown period (or normality 1 period); - [15/03/2019 – 17/05/2019] versus [15/03/2020 – 17/05/2020] → lockdown Phase 0 period; - [18/05/2019 – 31/05/2019] versus [18/05/2020 – 31/05/2020] → lockdown Phase 1 period; - [01/06/2019 – 14/06/2019] versus [01/06/2020 – 14/06/2020] → lockdown Phase 2 period; - [15/06/2019 – 20/06/2019] versus [15/06/2020 – 20/06/2020] → lockdown Phase 3 period; - [21/06/2019 – 30/06/2019] versus [01/01/2020 – 30/06/2020] → post- lockdown period (or normality 2 period). The results showed that the levels of air particulate matter did not significantly differ in the two years during the pre-lockdown (or normality 1) period in most of the air quality monitoring stations of Valencia. The only exceptions are represented by the air quality stations of the center of València, València Pista de Silla and València Vivers. for PM10, and València Avd Francia for PM2.5. Reductions of air concentrations of particulate matter less than 10 microns (PM10) were observed in all the monitoring stations, except for the air quality station of València Vivers, where an increase of PM10 was observed in 2020. Regarding the lockdown period, a decrease in the levels of particulate matter (PM10 and PM2.5) was observed in most of the air quality monitoring stations. The highest concentrations decline rates were observed in València Centre, València Avd Francia, and València Pista de Silla, all classified as traffic stations, in which decreases of 58%–42%, 56%–53%, and 60%–41% were observed respectively. Regarding nitric oxides, the levels of NOx, NO2, and NO significantly reduced during the lockdown period in all air quality monitoring stations of Valencia. More specifically, the concentrations of NOx, NO2, and NO significantly reduced of 48.5%–49.8%–46.2%, 62.1%–67.4%–45.7%, 37.4%–35.7%–35.3%, 60.7%–67.7%–47.1%, 65.5%–65.8%–63.5%, 60.0%–64.5%–41.3%, and 60.4%–61.6%–52.5% for València Centre, València Avd Francia, València Bulevard Sud, València Pista de Silla (all the urban traffic type), València Molí del Sol (suburban traffic type), València Politècnic (suburban background type), and València Vivers (urban background type) air monitoring stations respectively. Regarding ozone, a decrease in concentrations was observed in 2020 in most of the air quality monitoring stations. Conclusions In conclusion, the systematic reviews show the presence of a relationship between exposure to lead and particulate matter and attention-deficit/hyperactivity disorder in children. In fact, most of the studies included in the reviews reported a statistically significant positive association. However, we detected high variability among study designs and probably high risks of bias of exposure assessment. Based on these results, we need additional data to fully understand the nature of the relationship between lead and particulate matter exposure and attention- deficit/hyperactivity disorder in 20 children. Future research should consider all potentially confounding variables and use a standardized method of ADHD diagnosis. Moreover, future studies could be focused on lead and particulate matter exposure of the mothers during late pregnancy and the first years of the life of the children. Combined exposure to multiple chemicals should also be assessed along with the influence of genetic factors. Regarding the air pollution levels during the lockdown periods due to the Covid-19 pandemic, overall, we observed a decrease in the main pollutants of the urban areas. However, we noticed differences among the various areas and the type of pollutant, which should be considered by policymakers to adopt new urban policies to reduce pollution of cities and protect human health even after the COVID-19 crisis.