1 00:00:01,640 --> 00:00:04,040 The following content is provided under a Creative 2 00:00:04,040 --> 00:00:05,580 Commons license. 3 00:00:05,580 --> 00:00:07,880 Your support will help MIT OpenCourseWare 4 00:00:07,880 --> 00:00:12,270 continue to offer high quality educational resources for free. 5 00:00:12,270 --> 00:00:14,870 To make a donation or view additional materials 6 00:00:14,870 --> 00:00:18,830 from hundreds of MIT courses, visit MIT OpenCourseWare 7 00:00:18,830 --> 00:00:20,000 at ocw.mit.edu. 8 00:00:22,850 --> 00:00:25,790 RAJESH: My name is Rajesh. 9 00:00:25,790 --> 00:00:28,430 I am one of the co-founders of ClimateX, 10 00:00:28,430 --> 00:00:31,670 which is some of the groups who are sponsoring this event. 11 00:00:31,670 --> 00:00:36,650 We are really, really happy to have a wonderful cast 12 00:00:36,650 --> 00:00:40,510 of speakers, who will be talking to us about how to turn some 13 00:00:40,510 --> 00:00:43,820 of the work that we have all done in the field 14 00:00:43,820 --> 00:00:50,850 into advocacy, especially three kinds of advocacy. 15 00:00:50,850 --> 00:00:55,040 One is a citizen advocacy, which Nathan and Audrey 16 00:00:55,040 --> 00:00:56,390 will be talking about. 17 00:00:56,390 --> 00:00:59,540 Then there is legal advocacy, which is Chris. 18 00:00:59,540 --> 00:01:05,660 And advocacy by creating market solutions, which will be Ory. 19 00:01:05,660 --> 00:01:08,030 So I'm going to get out-- 20 00:01:08,030 --> 00:01:13,100 to prove that I'm not Donald Trump, I'll stop talking now 21 00:01:13,100 --> 00:01:16,830 and give it over to Chris, who is 22 00:01:16,830 --> 00:01:21,270 going to talk a little bit about how he, as an MIT chemical 23 00:01:21,270 --> 00:01:26,799 engineer, became a lawyer and is now going after the bad guys. 24 00:01:26,799 --> 00:01:28,840 CHRIS NIDEL: So as Rajesh said, my name is Chris. 25 00:01:28,840 --> 00:01:31,460 I'm an attorney in DC. 26 00:01:31,460 --> 00:01:34,220 I do mostly environmental work. 27 00:01:34,220 --> 00:01:37,097 I do some other pharmaceutical work, 28 00:01:37,097 --> 00:01:38,930 but my background's in chemical engineering. 29 00:01:38,930 --> 00:01:43,160 So I got a master's degree from MIT in chemical engineering, 30 00:01:43,160 --> 00:01:48,500 and I use that background to evaluate chemical exposures, 31 00:01:48,500 --> 00:01:51,830 to evaluate pharmaceutical exposures, to review-- 32 00:01:51,830 --> 00:01:54,920 and review of epidemiology, and toxicology, 33 00:01:54,920 --> 00:02:00,050 and related scientific subjects to the cases that I bring. 34 00:02:00,050 --> 00:02:04,520 And as part of that, I use the whole spectrum 35 00:02:04,520 --> 00:02:06,890 of scientific data. 36 00:02:06,890 --> 00:02:12,590 Some of that is very high tech, lab-based, mathematical 37 00:02:12,590 --> 00:02:17,060 modeling, obviously, you know, EPA type sampling. 38 00:02:17,060 --> 00:02:19,114 But then I also use-- 39 00:02:19,114 --> 00:02:20,780 at sort of the other end of the spectrum 40 00:02:20,780 --> 00:02:24,074 is more citizen science type data. 41 00:02:24,074 --> 00:02:26,240 We'll start by talking about a few examples starting 42 00:02:26,240 --> 00:02:29,840 with photography, but people don't think of photography 43 00:02:29,840 --> 00:02:30,680 as being data. 44 00:02:30,680 --> 00:02:34,320 But the same standards apply in court, 45 00:02:34,320 --> 00:02:38,120 whether it's a sample taken by an expert with multiple degrees 46 00:02:38,120 --> 00:02:41,900 to a picture taken by someone on the street corner. 47 00:02:41,900 --> 00:02:44,390 And so, I think photography is a good starting point 48 00:02:44,390 --> 00:02:47,330 to talk about how to get things admitted into court, 49 00:02:47,330 --> 00:02:53,630 and how you can use these types of information, data, 50 00:02:53,630 --> 00:02:57,620 to advocate for your cause-- to prove a case whether it's 51 00:02:57,620 --> 00:03:02,030 to oppose a permit, or to oppose an expansion of a facility, 52 00:03:02,030 --> 00:03:06,200 or to generate some finding of liability 53 00:03:06,200 --> 00:03:08,880 on behalf of your client. 54 00:03:08,880 --> 00:03:12,710 So we're going to see if I can do this with my phone, 55 00:03:12,710 --> 00:03:15,512 and it seems like it's working. 56 00:03:15,512 --> 00:03:16,970 The important thing, when you start 57 00:03:16,970 --> 00:03:20,530 talking about scientific evidence, 58 00:03:20,530 --> 00:03:22,250 I get all kinds of questions where 59 00:03:22,250 --> 00:03:24,080 someone will say, you know, we're 60 00:03:24,080 --> 00:03:25,760 looking to fight this issue. 61 00:03:25,760 --> 00:03:28,130 We want to go to court, and we want our data 62 00:03:28,130 --> 00:03:31,400 to be good in court. 63 00:03:31,400 --> 00:03:32,940 So we want data that's the best. 64 00:03:32,940 --> 00:03:34,670 We want it to be good in court. 65 00:03:34,670 --> 00:03:40,370 And the reality there's no golden rule 66 00:03:40,370 --> 00:03:43,460 for what's admissible in court. 67 00:03:43,460 --> 00:03:46,950 You can have-- I had a case in Massachusetts outside Boston, 68 00:03:46,950 --> 00:03:52,050 a PCB case where the EPA had done hundreds and thousands 69 00:03:52,050 --> 00:03:53,960 of sampling testing-- 70 00:03:53,960 --> 00:03:57,110 where they had sampled the ground for PCBs, 71 00:03:57,110 --> 00:03:59,600 and it was all EPA sampling. 72 00:03:59,600 --> 00:04:01,645 It was not part of litigation. 73 00:04:01,645 --> 00:04:04,160 It wasn't done by me or my experts, 74 00:04:04,160 --> 00:04:06,710 and despite the fact that it was EPA sampling 75 00:04:06,710 --> 00:04:09,410 done by EPA methods and protocols, 76 00:04:09,410 --> 00:04:11,270 the defendant still challenged the data. 77 00:04:11,270 --> 00:04:15,200 I mean, they say, well, it's not reliable because it was EPA. 78 00:04:15,200 --> 00:04:18,269 So there's no answer that says, well, 79 00:04:18,269 --> 00:04:22,940 if you do a, b, and c, and dot all the I's in there, 80 00:04:22,940 --> 00:04:24,440 that it will be admitted. 81 00:04:24,440 --> 00:04:27,230 And I think, to the other point is, 82 00:04:27,230 --> 00:04:30,020 if you haven't done a, b, and c, you still 83 00:04:30,020 --> 00:04:32,900 may get your data in front of the jury 84 00:04:32,900 --> 00:04:35,030 or in front of the judge, depending 85 00:04:35,030 --> 00:04:39,020 on the assessment of essentially these factors, which 86 00:04:39,020 --> 00:04:41,270 the first thing is whether the data is 87 00:04:41,270 --> 00:04:43,400 reliable and repeatable. 88 00:04:43,400 --> 00:04:46,670 So for example, I had a case, that I'll 89 00:04:46,670 --> 00:04:50,060 give you a couple examples from, that involved 90 00:04:50,060 --> 00:04:51,910 sampling for bacteria. 91 00:04:51,910 --> 00:04:54,480 It was a chicken house that had discharges 92 00:04:54,480 --> 00:04:55,820 going to the waters of the US. 93 00:04:55,820 --> 00:04:59,450 It was a Clean Water Act case, and there was a question about 94 00:04:59,450 --> 00:05:03,250 whether there was bacteria as well as some nutrients 95 00:05:03,250 --> 00:05:06,730 in the water-- so phosphorous and nitrogen-- 96 00:05:06,730 --> 00:05:08,590 that were polluting the waterways. 97 00:05:08,590 --> 00:05:13,270 And questions that become relevant are 98 00:05:13,270 --> 00:05:15,520 is a person wearing gloves, is there 99 00:05:15,520 --> 00:05:17,969 sterile procedures used, are the containers 100 00:05:17,969 --> 00:05:20,260 that they're going to take a sample of the water from-- 101 00:05:20,260 --> 00:05:21,850 are they sterile to begin with? 102 00:05:21,850 --> 00:05:24,790 Obviously, if there's any question about those things, 103 00:05:24,790 --> 00:05:28,570 it calls into question the reliability and repeatability 104 00:05:28,570 --> 00:05:29,660 of the data. 105 00:05:29,660 --> 00:05:32,320 So that's the first real prong. 106 00:05:32,320 --> 00:05:36,070 The second prong, and this is pretty loaded, 107 00:05:36,070 --> 00:05:38,720 is whether it's fit for the intended purpose. 108 00:05:38,720 --> 00:05:42,710 So if you're trying to prove, for example, 109 00:05:42,710 --> 00:05:46,630 the presence of benzene in somebody's drinking water, 110 00:05:46,630 --> 00:05:50,080 and you have a certain test that detects benzene, 111 00:05:50,080 --> 00:05:52,705 and maybe it gives it a number. 112 00:05:52,705 --> 00:05:54,580 If the question for the jury is whether there 113 00:05:54,580 --> 00:05:59,050 was benzene in the water, there may be a different standard 114 00:05:59,050 --> 00:06:01,120 than if the question is was there 115 00:06:01,120 --> 00:06:04,240 enough benzene in the water for the last 15 years 116 00:06:04,240 --> 00:06:06,970 to give my client leukemia? 117 00:06:06,970 --> 00:06:09,400 And so, there becomes this question 118 00:06:09,400 --> 00:06:11,500 about what is the purpose that you're 119 00:06:11,500 --> 00:06:15,130 offering the evidence for, which then 120 00:06:15,130 --> 00:06:18,820 relates this third point, which is given your purpose, 121 00:06:18,820 --> 00:06:21,100 is it going to assist the jury? 122 00:06:21,100 --> 00:06:25,180 So if that information, to whatever degree 123 00:06:25,180 --> 00:06:29,920 it falls in that scientific rigor spectrum, 124 00:06:29,920 --> 00:06:32,270 how is that going to help the jury understand? 125 00:06:32,270 --> 00:06:36,880 So maybe we have a test that shows the presence of benzene, 126 00:06:36,880 --> 00:06:38,360 but we don't trust the number. 127 00:06:38,360 --> 00:06:41,710 But we have an expert that does groundwater modeling that 128 00:06:41,710 --> 00:06:43,210 can say, well, we have a number that 129 00:06:43,210 --> 00:06:44,800 shows the presence of benzene. 130 00:06:44,800 --> 00:06:46,210 On top of that, we have an expert 131 00:06:46,210 --> 00:06:49,192 that modeled out the groundwater for the last 15 years 132 00:06:49,192 --> 00:06:51,400 and can show the fluctuations in those concentrations 133 00:06:51,400 --> 00:06:52,900 over the last 15 years-- 134 00:06:52,900 --> 00:06:55,900 the combination of which gives you 135 00:06:55,900 --> 00:06:58,690 the juror or the judge confidence that there 136 00:06:58,690 --> 00:07:00,400 was, in fact, benzene in the water, 137 00:07:00,400 --> 00:07:03,550 and it was within a certain range that, I might argue, 138 00:07:03,550 --> 00:07:06,820 caused a person to get sick. 139 00:07:06,820 --> 00:07:09,700 So with that being said, let's see. 140 00:07:09,700 --> 00:07:11,180 Oh, do I have-- 141 00:07:11,180 --> 00:07:13,640 is it going to be? 142 00:07:13,640 --> 00:07:16,180 Do I not know what I'm doing? 143 00:07:16,180 --> 00:07:16,930 OK. 144 00:07:16,930 --> 00:07:18,520 So here's a simple example. 145 00:07:18,520 --> 00:07:22,120 So I represent-- there's a lot of people in DC. 146 00:07:22,120 --> 00:07:25,360 There's probably a lot of people in a lot of different cities. 147 00:07:25,360 --> 00:07:27,070 It's not glamorous by any stretch, 148 00:07:27,070 --> 00:07:29,170 but there are a lot of people that 149 00:07:29,170 --> 00:07:31,570 live in subsidized housing in this country that 150 00:07:31,570 --> 00:07:35,210 is sub, sub, sub standard. 151 00:07:35,210 --> 00:07:37,090 So there are people that, whether it's 152 00:07:37,090 --> 00:07:39,670 the state or its HUD, the federal government, 153 00:07:39,670 --> 00:07:41,620 are paying for them. 154 00:07:41,620 --> 00:07:43,560 In a city like DC, they're paying, 155 00:07:43,560 --> 00:07:49,930 you know, $1,500 a month for a mother and her child 156 00:07:49,930 --> 00:07:55,030 to live in conditions that are unhealthy for both the mother 157 00:07:55,030 --> 00:07:58,330 and for the child, and that the government's paying 158 00:07:58,330 --> 00:08:00,700 the bill for, and somebody is profiting 159 00:08:00,700 --> 00:08:02,504 from at the expense of these people living 160 00:08:02,504 --> 00:08:04,420 in there and at the expense of the government. 161 00:08:04,420 --> 00:08:09,730 And so, this is an easy example, where someone takes a picture. 162 00:08:09,730 --> 00:08:11,740 I picked a picture of this as, you know, 163 00:08:11,740 --> 00:08:15,580 water coming through the ceiling of this apartment 164 00:08:15,580 --> 00:08:17,480 where you have mold. 165 00:08:17,480 --> 00:08:19,670 We are currently-- 166 00:08:19,670 --> 00:08:21,530 I don't know why we're losing our picture. 167 00:08:21,530 --> 00:08:23,620 Oh, there we go. 168 00:08:23,620 --> 00:08:27,830 So we represent-- in some cases, we represent the government 169 00:08:27,830 --> 00:08:30,310 on what's called the whistle blower lawsuit, where we're 170 00:08:30,310 --> 00:08:34,120 representing the government against these landlords 171 00:08:34,120 --> 00:08:36,280 for collecting money from the government, 172 00:08:36,280 --> 00:08:39,539 representing that these are habitable healthy places 173 00:08:39,539 --> 00:08:41,830 that they're getting paid a significant amount of money 174 00:08:41,830 --> 00:08:44,500 for-- for people to come, and get sick, and end up 175 00:08:44,500 --> 00:08:46,642 at the emergency room with asthma attacks 176 00:08:46,642 --> 00:08:48,850 when they can't breathe anymore, because they've been 177 00:08:48,850 --> 00:08:50,530 living in these conditions. 178 00:08:50,530 --> 00:08:53,150 I could have-- there was a horrible situation. 179 00:08:53,150 --> 00:08:57,190 And again, it's sort of a simplified approach to data, 180 00:08:57,190 --> 00:09:01,780 but in one of these apartments, you go in there, 181 00:09:01,780 --> 00:09:04,150 and there were so many bugs. 182 00:09:04,150 --> 00:09:05,700 I mean, you couldn't-- 183 00:09:05,700 --> 00:09:08,440 it was the craziest thing. 184 00:09:08,440 --> 00:09:10,870 But the picture is worth a thousand words, 185 00:09:10,870 --> 00:09:12,790 and there becomes a question, like there 186 00:09:12,790 --> 00:09:14,915 would be with any data, as to who took the picture? 187 00:09:14,915 --> 00:09:15,700 When was it? 188 00:09:15,700 --> 00:09:18,100 Can they certify that it was in the apartment? 189 00:09:18,100 --> 00:09:18,910 That's at issue. 190 00:09:18,910 --> 00:09:21,670 And at the time-- and, you know, that they didn't-- 191 00:09:21,670 --> 00:09:24,250 it wasn't a bunch of plastic cockroaches that they put, 192 00:09:24,250 --> 00:09:27,580 but it was in fact a bunch of bugs crawling around. 193 00:09:27,580 --> 00:09:30,270 Oh, and this, the farmer trenched the ditch. 194 00:09:30,270 --> 00:09:33,430 So the purpose here would be to show visible evidence of mold 195 00:09:33,430 --> 00:09:35,960 as opposed to the farmer trenching 196 00:09:35,960 --> 00:09:38,620 the ditch, which we'll get to. 197 00:09:38,620 --> 00:09:41,020 So you're going to have a witness that comes in and says, 198 00:09:41,020 --> 00:09:42,103 yeah, I took that picture. 199 00:09:42,103 --> 00:09:44,230 I took it three months ago. 200 00:09:44,230 --> 00:09:47,890 I remember because it was two weeks 201 00:09:47,890 --> 00:09:49,990 after the water was dripping from the ceiling, 202 00:09:49,990 --> 00:09:52,300 and here's the mold. 203 00:09:52,300 --> 00:09:55,360 Again, if we were trying to prove a housing code violation, 204 00:09:55,360 --> 00:09:56,927 the picture is probably sufficient. 205 00:09:56,927 --> 00:09:59,260 If we're trying to prove that somebody has asthma attack 206 00:09:59,260 --> 00:10:02,310 two weeks later was caused by this mold, that's 207 00:10:02,310 --> 00:10:04,560 where you get into some more sophisticated sampling 208 00:10:04,560 --> 00:10:06,390 and having an industrial hygienist 209 00:10:06,390 --> 00:10:09,010 come in and do some testing. 210 00:10:09,010 --> 00:10:13,950 So this is another type of litigation 211 00:10:13,950 --> 00:10:16,290 that I've been involved in. 212 00:10:16,290 --> 00:10:19,230 For those people that aren't aware, 213 00:10:19,230 --> 00:10:26,677 a good bit of our sewage sludge, human waste, that we generate 214 00:10:26,677 --> 00:10:28,260 goes from the sewage treatment plants, 215 00:10:28,260 --> 00:10:33,120 gets collected as solids, and then gets sent to farms. 216 00:10:33,120 --> 00:10:35,850 In this case, it was a quote unquote, tree farm, 217 00:10:35,850 --> 00:10:39,060 but it's sent to farms that grow crops. 218 00:10:39,060 --> 00:10:42,810 And they put human waste on crops-- 219 00:10:42,810 --> 00:10:45,510 the argument being that it's treated. 220 00:10:45,510 --> 00:10:47,030 The treating is relatively minimal, 221 00:10:47,030 --> 00:10:53,640 and they essentially put lime in with the fecal material. 222 00:10:53,640 --> 00:10:56,100 And they then use it as fertilizer. 223 00:10:56,100 --> 00:11:00,420 So as you can imagine it's full of pharmaceuticals-- 224 00:11:00,420 --> 00:11:01,860 it's full of industrial chemicals, 225 00:11:01,860 --> 00:11:05,602 because the local industries all contribute to the sewer system. 226 00:11:05,602 --> 00:11:07,310 There's hospital waste in there, so there 227 00:11:07,310 --> 00:11:09,360 is viruses, pathogens, all these things 228 00:11:09,360 --> 00:11:13,700 that you would expect in human feces-- 229 00:11:13,700 --> 00:11:14,560 exist in there. 230 00:11:14,560 --> 00:11:19,170 And in this case, this was a 1,300-acre forest that they 231 00:11:19,170 --> 00:11:22,500 spread this stuff in, and my client lived in the middle 232 00:11:22,500 --> 00:11:24,870 of the forest. 233 00:11:24,870 --> 00:11:28,260 She ended up with pulmonary fibrosis with her lungs 234 00:11:28,260 --> 00:11:31,680 that were getting scarred from repeated 235 00:11:31,680 --> 00:11:35,610 inflammation from breathing in these proteins and stuff. 236 00:11:35,610 --> 00:11:38,640 And so, this was one of her pictures of what was going on. 237 00:11:38,640 --> 00:11:40,740 Again, like, not what we necessarily 238 00:11:40,740 --> 00:11:45,420 think of with respect to data, but it 239 00:11:45,420 --> 00:11:48,030 creates a compelling picture, and you still 240 00:11:48,030 --> 00:11:50,790 have that same standard of getting 241 00:11:50,790 --> 00:11:53,580 it admitted for the court. 242 00:11:53,580 --> 00:11:56,320 This is a picture from the case that I referenced a little bit 243 00:11:56,320 --> 00:11:56,820 ago. 244 00:11:56,820 --> 00:12:02,670 This is a poultry operation that we have there 245 00:12:02,670 --> 00:12:05,790 that we had access on public property. 246 00:12:05,790 --> 00:12:09,000 The water keeper, or the local river keeper, 247 00:12:09,000 --> 00:12:11,970 had access on public property to this drainage ditch, 248 00:12:11,970 --> 00:12:15,030 and this is where we get into sort of a combined 249 00:12:15,030 --> 00:12:19,534 use of different media or data. 250 00:12:19,534 --> 00:12:21,450 The water keeper is able to come to this ditch 251 00:12:21,450 --> 00:12:26,490 on public property and take water samples, which she did, 252 00:12:26,490 --> 00:12:28,030 I think, 12 or 14 times. 253 00:12:28,030 --> 00:12:31,080 So she took water samples, which showed 254 00:12:31,080 --> 00:12:32,910 high levels of bacteria-- 255 00:12:32,910 --> 00:12:35,460 fecal coliform, and E. coli, as well as 256 00:12:35,460 --> 00:12:38,336 high levels of nutrients going through the ditch, 257 00:12:38,336 --> 00:12:39,960 across the highway, and then eventually 258 00:12:39,960 --> 00:12:43,860 out into the waters of the US. 259 00:12:43,860 --> 00:12:45,870 And what this photo actually is is this 260 00:12:45,870 --> 00:12:48,150 is a photo from Google Earth. 261 00:12:48,150 --> 00:12:51,760 It's just a Google Earth satellite photo. 262 00:12:51,760 --> 00:12:55,170 Another point of issue was, at the top corner there, 263 00:12:55,170 --> 00:12:57,630 there was a pile of some unknown material. 264 00:12:57,630 --> 00:13:00,240 And we'll see a close up in a later picture 265 00:13:00,240 --> 00:13:04,090 that sort of shows some of what was going on on the farm. 266 00:13:04,090 --> 00:13:06,810 And then you have the pictures of the chicken houses. 267 00:13:06,810 --> 00:13:10,350 And then you have what's also a cattle operation in the bottom 268 00:13:10,350 --> 00:13:14,950 right there that I'm going to show you another picture of it. 269 00:13:14,950 --> 00:13:18,720 So again, this is Google Earth. 270 00:13:18,720 --> 00:13:20,850 We went to trial in this case. 271 00:13:20,850 --> 00:13:25,290 We got these pictures admitted from Google Earth. 272 00:13:25,290 --> 00:13:26,850 And one of the things that we saw-- 273 00:13:26,850 --> 00:13:31,050 and I was deposing the defendants-- 274 00:13:31,050 --> 00:13:35,430 is that the property had poultry operation that was run by-- 275 00:13:35,430 --> 00:13:38,100 well, that was owned by Purdue-- 276 00:13:38,100 --> 00:13:42,660 run by their quote, unquote, family farmer. 277 00:13:42,660 --> 00:13:44,640 And we believe that that was discharging 278 00:13:44,640 --> 00:13:48,330 into that drainage ditch and out to where we sampled. 279 00:13:48,330 --> 00:13:53,070 There was also the cattle operation, which had probably 280 00:13:53,070 --> 00:13:55,590 some impact on the water. 281 00:13:55,590 --> 00:13:57,175 But what was interesting was-- 282 00:13:57,175 --> 00:13:58,800 as part of the Clean Water Act lawsuit, 283 00:13:58,800 --> 00:14:02,140 we had to notify them that we were planning to sue them. 284 00:14:02,140 --> 00:14:06,510 And at some point, what you see on the aerial photo-- 285 00:14:06,510 --> 00:14:08,700 as you can see, you know, tiny there, 286 00:14:08,700 --> 00:14:13,110 but basically the cattle were then moved from the cattle 287 00:14:13,110 --> 00:14:15,300 grazing area down in the far right-- 288 00:14:15,300 --> 00:14:17,250 if we the looked on the previous photo, 289 00:14:17,250 --> 00:14:20,670 where you see all the cattle markings from them in the two 290 00:14:20,670 --> 00:14:22,470 lower right fields-- 291 00:14:22,470 --> 00:14:26,080 to a feeder that's up in the top there. 292 00:14:26,080 --> 00:14:28,650 So you can see the cows parked around some hay 293 00:14:28,650 --> 00:14:31,520 bales or a feeder with a bunch of hay on there 294 00:14:31,520 --> 00:14:35,760 to create the impression that the cows weren't just 295 00:14:35,760 --> 00:14:36,810 over to the far right. 296 00:14:36,810 --> 00:14:39,739 They were more by this ditch even though-- 297 00:14:39,739 --> 00:14:41,280 and that's just something we randomly 298 00:14:41,280 --> 00:14:43,770 picked up on Google Earth. 299 00:14:43,770 --> 00:14:45,270 So when I'm in deposition with them, 300 00:14:45,270 --> 00:14:47,220 and I ask them, well, where do the cows hang out-- 301 00:14:47,220 --> 00:14:49,511 and they tell me, oh, they hang out all over the place. 302 00:14:49,511 --> 00:14:52,530 And you can see, and there was a whole series 303 00:14:52,530 --> 00:14:55,020 of photos, as you can imagine, from Google Earth that 304 00:14:55,020 --> 00:15:00,180 show that, consistent with the markings in these areas, 305 00:15:00,180 --> 00:15:02,620 that the cows and their feeders mostly 306 00:15:02,620 --> 00:15:08,000 are in here, some in here, and pretty rarely up in there. 307 00:15:08,000 --> 00:15:11,220 And so, this is the aerial photograph. 308 00:15:11,220 --> 00:15:14,860 So this is the pile that was at the top of that drainage ditch. 309 00:15:14,860 --> 00:15:17,290 This is the ditch that was in question. 310 00:15:17,290 --> 00:15:19,900 And so, we had the Google Earth photos, 311 00:15:19,900 --> 00:15:21,850 and then we also had aerial photos 312 00:15:21,850 --> 00:15:26,560 that the water keeper was able to get a flight 313 00:15:26,560 --> 00:15:29,260 and take some of their own photos. 314 00:15:29,260 --> 00:15:31,900 And here you had a pile which was 315 00:15:31,900 --> 00:15:34,210 sort of unknown, what it was, but it was pretty 316 00:15:34,210 --> 00:15:37,690 clear from these photos that the farmer had dug trenches 317 00:15:37,690 --> 00:15:40,540 to drain what was in those piles-- 318 00:15:40,540 --> 00:15:43,210 the runoff, the water that was accumulating there, 319 00:15:43,210 --> 00:15:44,620 into the farm runoff-- 320 00:15:44,620 --> 00:15:47,560 into the ditches that then, ultimately, 321 00:15:47,560 --> 00:15:49,360 went out to where we sampled. 322 00:15:49,360 --> 00:15:54,190 Then it ultimately went out to the waters of the US. 323 00:15:54,190 --> 00:15:57,790 So again, what's the purpose of this photo? 324 00:15:57,790 --> 00:15:59,750 What's the purpose of the previous photo? 325 00:15:59,750 --> 00:16:02,710 If the purpose is to show where the cows are, if the purpose is 326 00:16:02,710 --> 00:16:04,930 to show where they are in a given date 327 00:16:04,930 --> 00:16:07,950 given the image date from Google Earth, 328 00:16:07,950 --> 00:16:12,670 if the purpose here is to show that the farmer had indeed-- 329 00:16:12,670 --> 00:16:15,460 that there was drainage coming from this pile, which 330 00:16:15,460 --> 00:16:17,490 turned out to be-- 331 00:16:17,490 --> 00:16:19,420 it was actually sewage sludge. 332 00:16:19,420 --> 00:16:22,870 Unrelated case, but the farmer was also 333 00:16:22,870 --> 00:16:24,490 stockpiling sewage sludge. 334 00:16:24,490 --> 00:16:29,590 So it's material that's rich in nutrients as well as probably 335 00:16:29,590 --> 00:16:31,630 bacteria and pathogens. 336 00:16:31,630 --> 00:16:33,890 And he had trenched this, so is their drainage? 337 00:16:33,890 --> 00:16:35,140 What's the material? 338 00:16:35,140 --> 00:16:38,590 Did he intend to drain this material into the ditch? 339 00:16:38,590 --> 00:16:40,300 Those questions, I think, it's certainly 340 00:16:40,300 --> 00:16:43,140 is relevant, and fit for that purpose, 341 00:16:43,140 --> 00:16:45,580 and would be helpful to the jury. 342 00:16:45,580 --> 00:16:49,660 So this is the sampling point, and this became an issue 343 00:16:49,660 --> 00:16:50,660 for a couple of reasons. 344 00:16:50,660 --> 00:16:53,130 One is whether she was trespassing. 345 00:16:53,130 --> 00:16:55,600 Two is how she took the samples, as far 346 00:16:55,600 --> 00:16:57,430 as her sterile technique. 347 00:16:57,430 --> 00:17:00,580 Getting to the lab data that we use, we had, 348 00:17:00,580 --> 00:17:02,770 I think, it was 14 or 12 samples. 349 00:17:02,770 --> 00:17:05,099 And she went through, she wore gloves, 350 00:17:05,099 --> 00:17:07,480 she used sealed containers from the lab. 351 00:17:07,480 --> 00:17:09,349 She kept them cold. 352 00:17:09,349 --> 00:17:14,328 She had a chain of custody from the sampling point to the lab. 353 00:17:14,328 --> 00:17:16,369 And so, all of the questions where you say, well, 354 00:17:16,369 --> 00:17:17,420 do we need to hire a consultant? 355 00:17:17,420 --> 00:17:19,128 Do we need to pay somebody a lot of money 356 00:17:19,128 --> 00:17:23,170 to come in from another state and take a sample, 357 00:17:23,170 --> 00:17:25,119 or is it sufficient to have somebody that's 358 00:17:25,119 --> 00:17:30,340 got sort of the requisite training that would otherwise 359 00:17:30,340 --> 00:17:31,424 disqualify the sample? 360 00:17:31,424 --> 00:17:33,340 So if she didn't know about sterile technique, 361 00:17:33,340 --> 00:17:35,890 or if she hadn't worn sterile gloves, 362 00:17:35,890 --> 00:17:39,195 or protected the integrity of the sampling containers, 363 00:17:39,195 --> 00:17:41,320 those would be things that would call into question 364 00:17:41,320 --> 00:17:43,270 the reliability of the results. 365 00:17:43,270 --> 00:17:46,720 But the court ended up admitting the sample results-- 366 00:17:46,720 --> 00:17:48,250 didn't question the results-- 367 00:17:48,250 --> 00:17:50,860 because she was able to answer questions on her cross-exam 368 00:17:50,860 --> 00:17:53,080 that she had done all of the things 369 00:17:53,080 --> 00:17:56,920 that would otherwise disqualify those results. 370 00:17:56,920 --> 00:18:00,190 And so, there was some question about whether there was flow 371 00:18:00,190 --> 00:18:03,130 that the defendant said that there wasn't ever flow-- 372 00:18:03,130 --> 00:18:06,765 that there was barely ever water here. 373 00:18:06,765 --> 00:18:08,140 And so, these pictures, obviously 374 00:18:08,140 --> 00:18:10,960 came into play for that. 375 00:18:10,960 --> 00:18:13,810 This is at that public access point, 376 00:18:13,810 --> 00:18:17,750 and obviously that was helpful for that purpose. 377 00:18:17,750 --> 00:18:19,540 I just put this picture in here. 378 00:18:19,540 --> 00:18:20,380 I think Nathan-- 379 00:18:20,380 --> 00:18:22,740 I don't know if Nathan has an interest. 380 00:18:22,740 --> 00:18:25,320 Some of the work that I do on fracking-- 381 00:18:25,320 --> 00:18:27,850 and I do a lot of work with the folks down in Texas 382 00:18:27,850 --> 00:18:29,920 and the people in Pennsylvania. 383 00:18:29,920 --> 00:18:32,920 And so, it's difficult to see in this example. 384 00:18:32,920 --> 00:18:38,980 I just quickly pulled it off YouTube, but with FLIR camera, 385 00:18:38,980 --> 00:18:42,730 it's not unlike, in some sense, a standard photograph, which 386 00:18:42,730 --> 00:18:45,550 we've seen some examples of, but it's 387 00:18:45,550 --> 00:18:49,160 an $80,000 piece of equipment. 388 00:18:49,160 --> 00:18:53,350 But with such a camera, you can see emissions 389 00:18:53,350 --> 00:18:56,260 from this equipment that you can't see with the naked eye. 390 00:18:56,260 --> 00:18:58,030 And so, there's a piece of equipment here, 391 00:18:58,030 --> 00:19:00,310 and you can see all this billowing out 392 00:19:00,310 --> 00:19:03,250 of there, which is primarily volatile organic chemicals-- 393 00:19:03,250 --> 00:19:05,100 things like benzene, and toluene, 394 00:19:05,100 --> 00:19:08,710 and you name it from these storage tanks. 395 00:19:08,710 --> 00:19:10,684 And so, when you look at-- whether you're 396 00:19:10,684 --> 00:19:12,100 looking at climate change impacts, 397 00:19:12,100 --> 00:19:13,690 or whether you're looking at health 398 00:19:13,690 --> 00:19:19,540 and environmental impacts, or all of the above, you know, 399 00:19:19,540 --> 00:19:20,827 you have these tanks. 400 00:19:20,827 --> 00:19:21,910 You walk up close to them. 401 00:19:21,910 --> 00:19:23,470 You can smell them. 402 00:19:23,470 --> 00:19:25,780 But they are vented to the atmosphere. 403 00:19:25,780 --> 00:19:28,240 They're not being regulated. 404 00:19:28,240 --> 00:19:29,692 And you have people-- 405 00:19:29,692 --> 00:19:31,150 typically with respect to fracking, 406 00:19:31,150 --> 00:19:35,830 you have people that are living in conjunction with them 407 00:19:35,830 --> 00:19:37,420 in close proximity. 408 00:19:37,420 --> 00:19:41,170 You might have a swimming pool or a playground 409 00:19:41,170 --> 00:19:43,810 in somebody's side yard that's within a few hundred 410 00:19:43,810 --> 00:19:45,920 feet of some of these tanks. 411 00:19:45,920 --> 00:19:49,580 And so, again, what's the purpose? 412 00:19:49,580 --> 00:19:52,510 Is the purpose to show that, in fact, there are emissions? 413 00:19:52,510 --> 00:19:56,230 Is the purpose to show that the four-year-old is 414 00:19:56,230 --> 00:20:00,570 having breathing problems as a result of these emissions? 415 00:20:00,570 --> 00:20:04,320 From my perspective, if the purpose is to show emissions, 416 00:20:04,320 --> 00:20:06,410 you have someone testify. 417 00:20:06,410 --> 00:20:08,490 Five minutes? 418 00:20:08,490 --> 00:20:13,560 That's perfect-- you know, what the FLIR camera does, 419 00:20:13,560 --> 00:20:15,540 how it does it, and what it shows-- 420 00:20:15,540 --> 00:20:18,189 which I think would validate that there are admissions. 421 00:20:18,189 --> 00:20:20,730 If the purpose was to show that somebody was having breathing 422 00:20:20,730 --> 00:20:24,480 problems or was getting cancer from that, you would probably 423 00:20:24,480 --> 00:20:27,810 also have, for example, some other grab samples, some air 424 00:20:27,810 --> 00:20:31,180 testing, that actually qualified or quantified 425 00:20:31,180 --> 00:20:34,200 the amounts of benzene, and toluene, and other things that 426 00:20:34,200 --> 00:20:37,410 are in the air that would then be corroborated 427 00:20:37,410 --> 00:20:40,360 with this visual evidence that says, in fact, 428 00:20:40,360 --> 00:20:42,220 there is something coming from the tank. 429 00:20:42,220 --> 00:20:44,220 And we're picking it up in the front yard 430 00:20:44,220 --> 00:20:47,550 of my client or the person that's complaining. 431 00:20:47,550 --> 00:20:49,710 So I think that this is an example. 432 00:20:49,710 --> 00:20:52,454 Unfortunately, it's out of the hands-- 433 00:20:52,454 --> 00:20:54,870 financially out of the hands of most people at this point, 434 00:20:54,870 --> 00:20:57,450 but it's a really powerful tool with respect 435 00:20:57,450 --> 00:21:00,180 to not just health but also climate change 436 00:21:00,180 --> 00:21:04,200 issues with methane leaks and other things. 437 00:21:04,200 --> 00:21:07,930 This is an example outside Pittsburgh 438 00:21:07,930 --> 00:21:09,750 that I'm looking at right now. 439 00:21:09,750 --> 00:21:12,150 This is from modeling that was done-- 440 00:21:12,150 --> 00:21:14,910 and this is cancer risk based on Coke emissions. 441 00:21:14,910 --> 00:21:18,420 And so, I'm looking at a case around a Coke plant 442 00:21:18,420 --> 00:21:20,580 outside Pittsburgh, where you have 443 00:21:20,580 --> 00:21:24,660 people that are living in the shadows of this huge Coke 444 00:21:24,660 --> 00:21:27,240 plant, one of the few remaining in Pittsburgh. 445 00:21:27,240 --> 00:21:30,180 And this is sort of the rigorous data that's 446 00:21:30,180 --> 00:21:32,700 looked at based on air emission data and modeling 447 00:21:32,700 --> 00:21:36,780 of those emissions, where you can see these huge cancer risks 448 00:21:36,780 --> 00:21:40,030 as a result of the emissions. 449 00:21:40,030 --> 00:21:42,120 This is another thing that's going 450 00:21:42,120 --> 00:21:46,820 on in that same community, which is this is-- 451 00:21:46,820 --> 00:21:47,960 I think this is called-- 452 00:21:47,960 --> 00:21:50,040 I forget the name of this device, 453 00:21:50,040 --> 00:21:54,960 but it's one of these internet of things connected devices 454 00:21:54,960 --> 00:21:56,760 that looks at air quality. 455 00:21:56,760 --> 00:21:59,610 So is it is it lab-certified data? 456 00:21:59,610 --> 00:22:01,950 I mean, this example it said 200 high. 457 00:22:01,950 --> 00:22:04,200 The air quality is bad. 458 00:22:04,200 --> 00:22:07,710 Again, am I trying to show that half the neighborhood has 459 00:22:07,710 --> 00:22:09,420 asthma because of this, or am I trying 460 00:22:09,420 --> 00:22:12,030 to show that they have decreased the property values 461 00:22:12,030 --> 00:22:13,860 because their air quality is bad, 462 00:22:13,860 --> 00:22:18,600 or am I just trying to show that the Coke ovens are impacting 463 00:22:18,600 --> 00:22:20,100 the air quality locally? 464 00:22:20,100 --> 00:22:22,260 And then I have the other data, the previous data, 465 00:22:22,260 --> 00:22:25,260 where I have modeling to show the real nature 466 00:22:25,260 --> 00:22:29,070 and extent of risk and injury. 467 00:22:29,070 --> 00:22:31,240 This is another similar device. 468 00:22:31,240 --> 00:22:33,330 These are guys that I've talked to about trying 469 00:22:33,330 --> 00:22:35,520 to use in some of my cases, where 470 00:22:35,520 --> 00:22:38,580 you could take for a couple hundred, $200 or $300. 471 00:22:38,580 --> 00:22:43,440 This one's called a uHoo, and you could distribute these 472 00:22:43,440 --> 00:22:47,130 throughout a neighborhood and show that, for example, 473 00:22:47,130 --> 00:22:48,330 the pattern of emissions. 474 00:22:48,330 --> 00:22:51,331 You could show that the emissions, or the air 475 00:22:51,331 --> 00:22:53,580 pollutants, are at the highest level near the facility 476 00:22:53,580 --> 00:22:57,120 and decay as you get farther out from the facility. 477 00:22:57,120 --> 00:22:59,470 What you might also try to show are things 478 00:22:59,470 --> 00:23:00,720 like the pattern of emissions. 479 00:23:00,720 --> 00:23:02,850 So a lot of times, industries will-- 480 00:23:05,970 --> 00:23:07,710 you know, smart on their part-- 481 00:23:07,710 --> 00:23:10,050 will wait until between 2:00 and 5:00 482 00:23:10,050 --> 00:23:13,500 in the morning to let things fly. 483 00:23:13,500 --> 00:23:15,570 And so, I have a case in Michigan 484 00:23:15,570 --> 00:23:20,670 where the refinery there is alleged, 485 00:23:20,670 --> 00:23:22,380 by the people living near it-- 486 00:23:22,380 --> 00:23:24,106 between 2:00 and 5:00 in the morning, 487 00:23:24,106 --> 00:23:25,230 they smell the rotten eggs. 488 00:23:25,230 --> 00:23:27,720 They smell the chemicals, and they wake up 489 00:23:27,720 --> 00:23:29,310 with difficulty breathing. 490 00:23:29,310 --> 00:23:35,130 And so, again, you put something like this inside your house 491 00:23:35,130 --> 00:23:36,660 or just outside your house. 492 00:23:36,660 --> 00:23:38,160 It's capturing data. 493 00:23:38,160 --> 00:23:42,570 And is the data being used to prove that you got lymphoma 494 00:23:42,570 --> 00:23:44,850 or leukemia from that, or is it being 495 00:23:44,850 --> 00:23:49,470 used to show that every night, at between 2:00 and 4:00, 496 00:23:49,470 --> 00:23:50,710 you get a spike? 497 00:23:50,710 --> 00:23:54,690 Whatever that number is, it may not be scientifically credible 498 00:23:54,690 --> 00:23:59,370 to the 0.00, but does it show that there is some pattern 499 00:23:59,370 --> 00:24:00,240 that-- 500 00:24:00,240 --> 00:24:02,400 or when the weather changes, that you get a shift, 501 00:24:02,400 --> 00:24:04,800 and that weather is consistent with winds 502 00:24:04,800 --> 00:24:09,010 blowing from the facility to your point of testing. 503 00:24:09,010 --> 00:24:11,610 So again, it really gets back to this question 504 00:24:11,610 --> 00:24:15,150 about what is it that you're intending to use the data for? 505 00:24:15,150 --> 00:24:18,240 This is-- what do I have, like, two minutes? 506 00:24:18,240 --> 00:24:21,570 This is another example, when I gave a talk with Public Lab, 507 00:24:21,570 --> 00:24:23,880 that someone was doing in New York City, which 508 00:24:23,880 --> 00:24:25,920 I thought was really great-- 509 00:24:25,920 --> 00:24:27,660 was that they were using-- 510 00:24:27,660 --> 00:24:29,640 they were giving low-income people 511 00:24:29,640 --> 00:24:33,632 that were in subsidized housing these connected thermometers. 512 00:24:33,632 --> 00:24:35,590 And they were collecting data from their homes, 513 00:24:35,590 --> 00:24:38,070 so that in the winter they could see where 514 00:24:38,070 --> 00:24:39,780 people were living in sub-- 515 00:24:39,780 --> 00:24:42,140 you know, in freezing-- or maybe not freezing 516 00:24:42,140 --> 00:24:45,840 but uninhabitable conditions in their homes that were not 517 00:24:45,840 --> 00:24:47,280 being properly-- so they're living 518 00:24:47,280 --> 00:24:49,320 in public housing or subsidized housing, 519 00:24:49,320 --> 00:24:51,870 and they're not being treated properly. 520 00:24:51,870 --> 00:24:54,030 And so, it's really a simple-- you know, 521 00:24:54,030 --> 00:24:57,960 it's just a temperature test with a GPS tag that could show 522 00:24:57,960 --> 00:24:59,820 these homes-- 523 00:24:59,820 --> 00:25:02,560 and I guess a time tag as well, so time and temperature 524 00:25:02,560 --> 00:25:04,860 are essentially that we could do, 525 00:25:04,860 --> 00:25:07,200 for the most part, with our smartphones these days-- 526 00:25:07,200 --> 00:25:10,200 that set up legal arguments for these people 527 00:25:10,200 --> 00:25:12,660 to say that they're not being treated properly 528 00:25:12,660 --> 00:25:16,140 and that they need adequate housing. 529 00:25:16,140 --> 00:25:20,640 So to sort of bring this a little bit to a head, 530 00:25:20,640 --> 00:25:26,814 better doesn't always mean more, and sometimes more data, 531 00:25:26,814 --> 00:25:28,230 even if it's qualitative data, can 532 00:25:28,230 --> 00:25:31,420 be better to prove a certain point, 533 00:25:31,420 --> 00:25:33,962 depending on what that point is. 534 00:25:33,962 --> 00:25:35,670 And so, there's this balance, or tension, 535 00:25:35,670 --> 00:25:39,000 between quality and quantity of data that largely relates back 536 00:25:39,000 --> 00:25:40,920 to money. 537 00:25:40,920 --> 00:25:43,380 So sort of to give you an example, 538 00:25:43,380 --> 00:25:45,880 this is that Coke facility in the top right. 539 00:25:45,880 --> 00:25:47,530 You can see part of it. 540 00:25:47,530 --> 00:25:50,670 And if we were to go and take samples 541 00:25:50,670 --> 00:25:53,790 throughout the neighborhood, they all cost money. 542 00:25:53,790 --> 00:25:55,500 If we were to take them with a PhD 543 00:25:55,500 --> 00:25:57,120 from the University of Pittsburgh, 544 00:25:57,120 --> 00:25:59,095 it would cost even more money. 545 00:25:59,095 --> 00:26:00,720 And so, the question becomes what is it 546 00:26:00,720 --> 00:26:03,000 that we are trying to prove? 547 00:26:03,000 --> 00:26:06,690 And if we're trying to prove that the levels of air 548 00:26:06,690 --> 00:26:09,330 pollutants are highest near the facility, 549 00:26:09,330 --> 00:26:11,430 and they decay as you get farther away, 550 00:26:11,430 --> 00:26:15,919 and that they spike at night, that that may be-- 551 00:26:15,919 --> 00:26:17,460 a certain level of data quality might 552 00:26:17,460 --> 00:26:19,350 be suitable for that versus trying 553 00:26:19,350 --> 00:26:22,050 to prove that someone's lung cancer or some other health 554 00:26:22,050 --> 00:26:27,100 issues were a result of that exposure. 555 00:26:27,100 --> 00:26:31,800 And I guess the converse is it may, in times-- 556 00:26:31,800 --> 00:26:33,750 and I think this is a good example-- 557 00:26:33,750 --> 00:26:39,360 be better to have more data of an arguable lesser quality 558 00:26:39,360 --> 00:26:44,430 than to have rigorous sampling done on three houses 559 00:26:44,430 --> 00:26:46,590 but not to have that full spectrum that 560 00:26:46,590 --> 00:26:49,980 shows what's happening as you get farther and farther away, 561 00:26:49,980 --> 00:26:52,680 because you focused your time, effort, and ultimately 562 00:26:52,680 --> 00:26:55,870 your money on sampling those things that 563 00:26:55,870 --> 00:26:59,500 were located where you had one sick person and that's it. 564 00:26:59,500 --> 00:27:00,750 Then you get into a question-- 565 00:27:00,750 --> 00:27:03,840 OK, I've got a great number outside this person's house, 566 00:27:03,840 --> 00:27:06,180 but I'm not sure how it relates back to the facility, 567 00:27:06,180 --> 00:27:08,513 and I'm not sure how the facility is impacting everybody 568 00:27:08,513 --> 00:27:10,300 else in the neighborhood. 569 00:27:10,300 --> 00:27:15,750 And I think that is-- so I think this is my last slide. 570 00:27:15,750 --> 00:27:18,900 Obviously, more money you pay for people 571 00:27:18,900 --> 00:27:21,990 with degrees, and consultants, and for lab testing-- 572 00:27:21,990 --> 00:27:26,520 you can save money with some of these techniques, citizen 573 00:27:26,520 --> 00:27:29,730 science, some of these taking your own samples 574 00:27:29,730 --> 00:27:32,310 with the appropriate training and technique-- 575 00:27:32,310 --> 00:27:34,050 using some of these connected devices, 576 00:27:34,050 --> 00:27:36,875 using some photography and FLIR cameras. 577 00:27:40,180 --> 00:27:43,740 More money doesn't always mean better, 578 00:27:43,740 --> 00:27:45,660 but it tends to mean that you've got 579 00:27:45,660 --> 00:27:48,690 better numbers, more precise numbers, that 580 00:27:48,690 --> 00:27:49,950 are more reliable. 581 00:27:49,950 --> 00:27:53,190 But again, the question is what is your purpose? 582 00:27:53,190 --> 00:27:55,606 If your purpose is to show you know 583 00:27:55,606 --> 00:27:57,480 that the source of the pollution is impacting 584 00:27:57,480 --> 00:27:59,550 the neighborhood in a broad sense, 585 00:27:59,550 --> 00:28:04,950 you can probably get by with some data of lesser rigor 586 00:28:04,950 --> 00:28:06,360 and that's lesser expense. 587 00:28:06,360 --> 00:28:08,120 If you're trying to prove someone's 588 00:28:08,120 --> 00:28:11,250 been exposed for how long at what level 589 00:28:11,250 --> 00:28:13,540 precisely to show that they got sick, 590 00:28:13,540 --> 00:28:15,810 obviously the standards are increased, 591 00:28:15,810 --> 00:28:20,530 and the level of scientific rigor is heightened as well. 592 00:28:20,530 --> 00:28:24,900 So I think that's it for me, and I'll pass-- 593 00:28:24,900 --> 00:28:27,160 I'll answer any questions. 594 00:28:27,160 --> 00:28:28,125 I'll receive applause. 595 00:28:32,900 --> 00:28:36,610 The question was to what extent trespassing 596 00:28:36,610 --> 00:28:39,100 on people's land and drones-- 597 00:28:39,100 --> 00:28:40,630 it's a good question. 598 00:28:40,630 --> 00:28:43,900 Drones are, you know, for some people, maybe 599 00:28:43,900 --> 00:28:48,045 not so new, but as far as people like me, a newer thing. 600 00:28:48,045 --> 00:28:49,420 But, I mean, drones are a great-- 601 00:28:49,420 --> 00:28:50,470 I mean, huge. 602 00:28:50,470 --> 00:28:54,020 Like, the aerial photos that were taken with a full flight-- 603 00:28:54,020 --> 00:28:56,020 like, having to rent a plane and all that stuff, 604 00:28:56,020 --> 00:28:58,300 it could be done with drones very easily-- 605 00:28:58,300 --> 00:29:00,250 get even better quality pictures. 606 00:29:00,250 --> 00:29:04,360 Drones are a huge, huge benefit to the citizen science 607 00:29:04,360 --> 00:29:05,219 operation. 608 00:29:05,219 --> 00:29:07,510 I mean, they have drones that can also take air samples 609 00:29:07,510 --> 00:29:11,980 and things like that, so the ability to get data 610 00:29:11,980 --> 00:29:14,740 cheaply, and efficiently, and effectively is a big thing. 611 00:29:14,740 --> 00:29:19,090 As far as trespassing goes, in my opinion, 612 00:29:19,090 --> 00:29:20,522 you never want to trespass. 613 00:29:20,522 --> 00:29:21,980 You never want to represent someone 614 00:29:21,980 --> 00:29:24,370 who's going to have somebody else saying, well, 615 00:29:24,370 --> 00:29:25,690 you trespassed. 616 00:29:25,690 --> 00:29:29,092 By the same token, I have had cases 617 00:29:29,092 --> 00:29:31,300 where people trespass on the land, and took a sample, 618 00:29:31,300 --> 00:29:31,990 and went home. 619 00:29:31,990 --> 00:29:34,124 And as a lawyer, I have an obligation 620 00:29:34,124 --> 00:29:36,040 to disclose the fact that they took a sample-- 621 00:29:36,040 --> 00:29:37,930 the result of that sampling. 622 00:29:37,930 --> 00:29:43,870 And aside from any allegations or claims of criminal trespass, 623 00:29:43,870 --> 00:29:45,790 if the data is reliable-- if they 624 00:29:45,790 --> 00:29:51,220 can't impugn what the person did-- that they somehow 625 00:29:51,220 --> 00:29:55,030 adulterated the sample, then the sample 626 00:29:55,030 --> 00:29:57,250 should be admissible for the purpose of showing 627 00:29:57,250 --> 00:29:58,750 what the sample shows, right? 628 00:29:58,750 --> 00:30:01,840 So there could be a dispute about whether there's 629 00:30:01,840 --> 00:30:04,090 some criminal trespass, which I think largely there's 630 00:30:04,090 --> 00:30:08,320 no impact from, but in general, the answer is if you can do it 631 00:30:08,320 --> 00:30:10,600 with a drone, and you can take pictures, 632 00:30:10,600 --> 00:30:12,250 if you can get a court order to get 633 00:30:12,250 --> 00:30:14,770 a sample on the property, that's a much better way 634 00:30:14,770 --> 00:30:16,730 to do it than trespassing. 635 00:30:16,730 --> 00:30:18,730 A lot of people don't understand the first thing 636 00:30:18,730 --> 00:30:21,500 you have to do is make a good case to the judge. 637 00:30:21,500 --> 00:30:25,270 So judges stand between you and the jury 638 00:30:25,270 --> 00:30:28,120 as far as scientific evidence-- as far as any evidence, right. 639 00:30:28,120 --> 00:30:31,840 And so, you first have to make a good argument to the judge 640 00:30:31,840 --> 00:30:35,140 that the data is reliable, fit for its intended purpose, 641 00:30:35,140 --> 00:30:36,790 and that it will assist the jury. 642 00:30:36,790 --> 00:30:38,650 What a jury will believe is oftentimes 643 00:30:38,650 --> 00:30:41,380 different than what a judge will believe, 644 00:30:41,380 --> 00:30:45,830 so something like a FLIR camera that shows visually 645 00:30:45,830 --> 00:30:48,440 the stream of pollution coming off 646 00:30:48,440 --> 00:30:50,590 may be very compelling to a jury even 647 00:30:50,590 --> 00:30:53,290 if I'm talking about leukemia. 648 00:30:53,290 --> 00:30:55,030 It may not be very compelling to a judge 649 00:30:55,030 --> 00:30:57,220 if I'm talking about leukemia, because it doesn't give you 650 00:30:57,220 --> 00:30:57,790 a number. 651 00:30:57,790 --> 00:30:59,830 It just tells you that there's stuff coming off, 652 00:30:59,830 --> 00:31:02,830 and I have an expert that says that stuff is this stuff that 653 00:31:02,830 --> 00:31:04,090 may cause leukemia. 654 00:31:04,090 --> 00:31:07,540 But it's still not 20 parts per billion of benzene, 655 00:31:07,540 --> 00:31:10,570 and that's ultimately what I need to show according 656 00:31:10,570 --> 00:31:12,520 to most judges, right. 657 00:31:12,520 --> 00:31:17,890 So I think, again, it's a very wide spectrum. 658 00:31:17,890 --> 00:31:23,710 It would vary hugely from hugely from judge to judge, 659 00:31:23,710 --> 00:31:26,110 and there's not any one answer. 660 00:31:26,110 --> 00:31:30,980 I can tell you, that as far as epidemiology goes, generally, 661 00:31:30,980 --> 00:31:34,240 what courts require-- if I want to show that an exposure caused 662 00:31:34,240 --> 00:31:36,640 my client's leukemia, for example, 663 00:31:36,640 --> 00:31:39,580 I would need to have a study that showed a greater 664 00:31:39,580 --> 00:31:42,040 than two-fold increased risk. 665 00:31:42,040 --> 00:31:44,110 So I have more than a doubling of the risk 666 00:31:44,110 --> 00:31:46,630 to a 95% confidence interval. 667 00:31:46,630 --> 00:31:51,790 So to eliminate the no risk and to be at least a doubling 668 00:31:51,790 --> 00:31:54,940 at that confidence interval, and that essentially 669 00:31:54,940 --> 00:31:57,220 goes to this argument that it's more probably than not 670 00:31:57,220 --> 00:31:59,100 caused by the exposure. 671 00:31:59,100 --> 00:32:01,810 Does that makes sense? 672 00:32:01,810 --> 00:32:03,360 OK.